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Approaches towards a theory of innovation in evolution
Evolutionary biologist Andreas Wagner writes in his book "The Origins of Evolutionary Innovations: A Theory of Transformative Change in Living Systems" (Oxford University Press, 2011):

We are familiar with many examples of innovations (qualitatively new
  phenotypes that can provide a critical benefit) but have no systematic
  understanding of the principles that allow organisms to innovate.

About the term innovation itself he writes:

It may be difficult to define rigorously what an evolutionary
  innovation is [538,616]. However, these and countless other examples
  show that it is usually easy to recognize: a new feature that endows
  its bearer with qualitatively new, often game changing abilities.
  These may not only mean the difference between life and death in a
  given environment (just think of biosynthetic abilities), they may
  also create broad platforms for future innovations, as did innovations
  of photosynthesis and of complex nervous systems.

and at another point:

Nature’s many innovations—some uncannily perfect—call for natural
  principles that accelerate life’s ability to innovate.
Can random mutations over a mere 3.8 billion years really be
  responsible for wings, eyeballs, knees, camouflage, lactose digestion,
  photosynthesis, and the rest of nature’s creative marvels? And if the
  answer is no, what is the mechanism that explains evolution’s speed
  and efficiency?

He then tries to develop such a framework for this crucial ingredient without which our understanding of the evolutionary process seems incomplete.
My question
Have Professor Wagner's hypotheses about innovation in evolution (what he calls a "theory of innovation") been corroborated empirically and are there other approaches towards a "theory of innovation" (according to his definition)?
NB
Definitions about what Prof. Wagner means by "innovation" and "theory of innovation" can be found in the above mentioned book, see e.g. here.
A brief overview of Wagner's "theory of innovation" can be found in the answers to my follow-up question: Key points of Wagner's theory of innovation

Does he present in his book an argument that random mutation is insufficient? The phrase "uncannily perfect" is horribly subjective and the word "mere" to describe 3.8 billion years is jarring and counterintuitive.

I upvoted because even if I question the premise of the book that doesn't mean the OP has asked a poor question. A negative score seems punitive  if unaccompanied by an explanation.

I downvoted because I feel the question is not well formulated and, without even a brief explanation of the theory, is unclear as a stand alone piece (requires reading a lot of external material before it can be understood). I requested clarification giving you the opportunity to improve the question before voting, and you declined said opportunity, so I downvoted. I think that is fair enough.

"..it appears that his theory of innovation is that mutation is potentially non-random in its effect - is this how you interpret it?" this was one of my requests for clarity. That was my interpretation based on what I have read of his work, and what I know from my PhD in evolutionary genetics. I felt that your question required an unreasonable amount of highly specific knowledge, so I requested a clarification, twice. As far as I am concerned that was a legitimate reason to downvote.

Can random mutations over a mere 3.8 billion years really be responsible for wings [...]? This is an argument from incredulity, a logical fallacy. Just because you can't imagine something happening, doesn't mean it can't happen.

@vonjd You should read some of Wagner's papers. A part of his research work is about possible mechanisms of evolutionary adaptation.  I haven't read this book but have read some of his articles. His articles on this topic are based on mathematical models and inference of bioinformatics data. The question does not deserve 6 downvotes but is a bit unclear to me.

@vonjd This is not same as the case of differential equation. Wagner's specific research is not that basic. Your question should be clear in itself. If you use a definition that is possibly not a textbook definition then quote it (along with citation). One does not need to be an expert on Wagner's research to answer this. You just need to provide the context. Wagner does not postulate any special theory.  He just uses the word innovation to mean evolutionary adaptations at a molecular scale. Did you check out his articles?

@vonjd please address WYSIWYG's concerns in his last comment. On Stack Overflow (and other Stack Exchange sites) there is the concept of an MCVE, a Minimal, Complete, and Verifiable Example. Questions need to be self-contained - don't post your code on pastebin, put it in the question. Don't link to your input file elsewhere (unless it's very large or in binary), put it in the question. You have asked a rather vague question about a random book, and seem to expect all of us to be fully up-to-date on Wagner's ideas. Providing a summary is common sense.

@vonjd additionally, your Google Books link is broken. Please fix it, and in the meantime, please summarize the contents of the link, as is expected on many other SE sites. The link should merely be a reference, in case it is moved or otherwise breaks.

The flaw in his argument, from what I can see in your quotes, is to equate evolution to natural selection. Natural selection was never proposed to explain all evolution, nor how advantageous traits arise, but was proposed to explain how advantageous traits spread. Ultimately, why adaptation is so prevalent?
The modern theory of evolution is so much more than a theory of evolution by natural selection. It can explain perfectly well how all variation has come in to being. Its mechanisms of mutation, migration, drift, and selection can both erode and generate new genetic variation. New genetic variation, and the phenotypic changes that brings, occurs by mutation. Various types of mutation can occur.
Mutations are random in their effect. Now it is possible that mutation rates are not random; if you imagine two theoretical species which have both gone through genetic bottlenecks, such that they both have little genetic variation, then the species with a higher mutation rate might have a better chance of staving off extinction because mutation will increase genetic variation, and genetic variation limits the rate of adaptation. Therefore, if there is a genetically coded trait which increases mutation rates then it may spread. Conversely, mutation is generally detrimental (see Fisher's Geometric Model) so, in general, there will also be selection for lower rates of mutation. From what I can gather on Wagner's theory on innovation is that mutation may be some kind of premeditated, directed mechanism with the organism trying to make a certain trait evolve. This is generally considered to be highly unlikely within the evolutionary biology community and, as far as I am aware, there is no evidence of it occurring.

Related posts I've made on biology SE:

Are humans evolving since their first appearance?
Why do some bad traits evolve, and good ones don't?
How is evolution possible in contemporary humans?
If evolution is not about increased complexity, why does so much complexity evolve?
Genetic Diversity and Adaptation
Evolution- Variations, adaptations and selective advantages
Evolution in 37 years, is it possible?

Having read some more on his theory, I think I am more clear on what he means. He uses the term innovation as other evolutionary biologists would use adaptation. I've no idea why he does this, there's no need to be using divergent terminology. Ultimately he is proposing adaptation by natural selection.

"Very occasionally, one or more mutations may also bring forth evolutionary innovations—new phenotypes that are qualitatively different and superior to the existing phenotypes."

"Evolutionary innovations can be difficult to define rigorously [1,2], but they are often easy to recognize as qualitatively new and adaptive traits of organisms. In addition, they also frequently provide new platforms upon which further evolutionary change can unfold."

The problem with this comes because he uses his own preconception that time simply has not been sufficient to allow all of the genetic variation to arise, to insinuate that random mutation is insufficient.

"Random change by itself is not sufficient, because it does not necessarily bring forth beneficial phenotypes."

The writing is suggestive (strong emphasis on suggestive, he never states this as far I can tell but the writing lends itself to misconception) that he will invoke some kind of directed mutation argument. However, his actual idea seems to be that genes interact and therefore this will increase the rate at which mutations cause phenotypic change. Because there is temporal and spatial variation not just in genetic variation, but also in selection, neutral mutations may accumulate in genes which later have an effect on fitness (either by interacting with new mutations elsewhere in the genetic network, or by shifts in the adaptive landscape).

"Wagner has emphasized how epistasis – interactions among genes that shape the adaptive consequences of mutations – allows neutral variation to modulate the effects of other mutations, producing a vast range of possible mutant phenotypes that may lead to an adaptive innovation."

This is nothing particularly new or controversial (see epistasis, gene networks, and read on evolution from standing vs. novel genetic variation). Basically, Wagner appears to suggest that Darwin's theory relies on novel mutation arriving after selection changes, which it does not, and Wagner's theory is simply a way for standing genetic variation to persist through gene networks. I don't think the concept of standing genetic variation is novel here, but the possibility that standing genetic variation may come under selection because of further mutation elsewhere in the gene network does appear to be novelty of his work.

MattDMo makes an excellent point on Wagner's assertion that all mutations occurring at random over just 3.8b years is implausible:

This is an argument from incredulity, a logical fallacy. Just because you can't imagine something happening, doesn't mean it can't happen.

"The flaw in his argument is to equate evolution to natural selection." No, he doesn't but I agree that the quote above seems to lead in this direction, it is a little bit too catchy. I think many of the mechanisms you refer to are in his book too. The thing he tries to achieve is to create a consistent framework for these phenomena in his theory of innovation.

Hi @vonjd - I was just in the middle of editing when you commented, just so happens that includes that sentence :) could you give an explanation in your question of what his theory of innovation is?

No problem. When you click on the link I gave you will find a good overview in the sections "innovations everywhere", "Towards a theory of innovation", "What must a theory of innovation accomplish?" and "What kind of information does a theory of innovation need?" but perhaps there are other approaches out there? This is the second part of my question.


text:
If evolution is not about increased complexity, why does so much complexity evolve?
In my last question I asked why we don't see increased complexity in artificial life simulations of evolution. It seems I had fallen for a common misconception, that evolution was about improvement by increasing complexity. One comment discussing that post read

"... he [David Deutsch] is falling for one of the biggest
  misconceptions about evolution that you can, that evolution is about
  improvement. Evolution has simply only ever been about change..."

However, when you look at the history of life you see increases in complexity. You see this increasing complexity evolving over billions of years, suggesting that it requires an explanation.
My question
If evolution is not about increasing complexity then how does so much complexity evolve?

The word improve can mean many things. There are many ways to "improve" - developing intelligence, flying, surviving extreme temperatures, good eyesight, good sense of smell, fast running, echolocation, etc. We just happened to develop some of those. I think it's better to call it complexity than improvement.

Comments are not for extended discussion; this conversation has been moved to chat.

@KyleStrand The question had just too many long comments which nobody would care to read. Moreover, as I can discern, comments and answers are getting repetitive. The usual SE practice is that when the OP addresses the point raised in the comment, you delete the comment. We are not hostile towards this question; unfortunately this topic is such that everyone wants to give their opinion whether or not they have a proper understanding of the subject. Moreover when a question hits HNQ, you have to be really careful. If we were really hostile then we would have closed the question.

You need to clarify what you mean with 'improvement', as the answers show. This is a vague term, and it is unclear if you are referring to improvement as in higher fitness, higher complexity (which is in itself also rather vague) or something else.

Change happens for the "benefit" or "detriment" of an individual. If it is detrimental, that individual stands less of a chance of contributing to the species' "gene-pool". Therefore beneficial changes are overrepresented in "gene-pools". Am I missing something here?

Not all changes are beneficial or detrimental @James - selection is often very weak, few mutations have large fitness effects.

@rg255 I see your point. A better way of talking about this change would be avoiding "molecular changes". Or rather saying that many molecular changes that contribute to net beneficial or detrimental changes in characteristics over large timescales.

I think possibly the problem here is the way you're approaching the issue.
You're considering improvement as anything that increases the abilities or complexity of the organism—that isn't necessarily what an improvement is though. The outcome of natural selection is that the organism best equipped to survive/reproduce in a certain environment is the most successful. So, for example, thermophillic archaea do much better in 60°C-plus pools of water than humans do. Our capacity to process information, use tools, etc. doesn't actually confer much advantage in that situation. And there can be downsides to that kind of complexity as well, requiring more energy and longer developmental periods. So, natural selection in 60°C-plus pools of water gives you archaea, and in (presumably) the plains of East Africa, it gives you humans.
The comment you quote mentions sickle-cell anaemia, which is a different example. While there is little benefit to having the sickle-cell anaemia allele in a temperate region, in those regions where malaria is endemic, heterozygosity can provide a survival advantage, and so the allele is maintained in the population. If you're someone living in a malaria-endemic region, and you don't have access to antimalarials, heterozygosity for the sickle-cell anaemia allele is arguably an improvement. It depends entirely on how you define the word.
The fundamental principal of natural selection is that it favours the organism most suited to a particular environment. But, that isn't always the most complex organism. It's important not to confuse human-like with better. It isn't the universal endpoint of evolution to produce an organism similar to us, just the organism most suited to the environment in question.
Also, to briefly address the previous question you asked—you asserted that we must be missing something from the process of evolution because we were unable to simulate it. You also pointed out that (in your opinion) we have sufficient computing power to simulate the kinds of organisms you're referring to. But natural selection is intrinsically linked to the environment it occurs in, so the simulation wouldn't just have to accurately simulate the biological processes of the organism, but also all of the external pressures the organism faces. I'd imagine that, in simulating evolution, that would be the real obstacle.

Comments are not for extended discussion; this conversation has been moved to chat.

Evolution is simply a process of change.  It is a change in trait values of populations over time. It results from four mechanisms: mutation, migration, drift, and selection. The first three lead to random change from one generation to the next, which may increase or decrease fitness, while selection will generally lead to adaptation (relatively increased fitness in subsequent generations).

"Evolution means change, change in the form and behaviour of organisms
  between generations. ... When members of a population breed and
  produce the next generation we can imagine a lineage of populations,
  made up of a series of populations through time. Each population is
  ancestral to the descendant population in the next generation: a
  lineage is an ancestor-descendent series of populations. Evolution is
  then change between generations within a population lineage." - Ridley,
  Evolution, Page 4.

This is what Darwin termed "descent with modification". Later in Ridley's book he goes on to say something which is important to for evolutionary biology; why is there so much adaptation?

".. not every detail of an organism's form and behaviour is
  necessarily adaptive. Adaptations are, however, so common that they
  have to be explained. Darwin regarded adaptation as the key problem
  that any theory of evolution had to solve. In Darwin's theory - as in
  modern evolutionary biology - the problem is solved by natural
  selection." - Ridley

Another good clue as to what evolution really is comes from the Charlesworth & Charlesworth book:

"Evolution means cumulative change over time in the characteristics of
  a population of living organisms. ... All evolutionary changes require
  initially rare genetic variants to spread among the members of a
  population, rising to high frequency..." Charlesworth & Charlesworth,
  Elements of Evolutionary Genetics, page XXV

Basically the random mechanisms of evolution (mutation, migration, drift) are not as good at making rare beneficial alleles spread through a population as selection is. Selection is the major mechanism that should, as a general rule, fix beneficial alleles in a population. Drift, mutation, and migration will rarely cause the beneficial (adaptive) alleles to fix. Furthermore, mutation will generally have deleterious (maladaptive) effects according to Fisher's geometric model of adaptation.
You can read more about the process of adaptation and why selection doesn't guarantee adaptive evolution in my answer here. Briefly, selection will lead to adaptation if there is sufficient genetic variance in fitness, selection is a constant from one generation to the next, and genetic correlations do not impede the response to selection. Furthermore, the other evolutionary mechanisms can counteract selection, preventing adaptation. These are some of the reasons that simulating evolution accurately is so difficult.
The reason that we can't say complexity increases by evolution is that none of these mechanisms give a consistent increase in complexity. While mutation, migration, and drift will have random effects on organismal complexity, fitness (thus selection) may have some relation to complexity. To evolve, some degree of complexity is required such that the minimum conditions for evolution can be met. However, selection should favour the most fit genes over time, which depends on the niche/adaptive landscape and genetic variation available. Selection in the real world (as opposed to alife* world) would, as an approximate rule of thumb, favour an intermediate level of complexity where fitness is optimised (individuals are good at producing offspring in their niche) with minimal wasteful complexity (complex structures that do not increase fitness).
In summary, to answer your question, we see so much improvement because of selection, which leads to the process of adaptation, but adaptation does not equate to increasing complexity. The key to understanding your problem is an understanding of the difference between the process of evolution (change) and the process of adaptation (improvement), and the difference between optimality and complexity. In the world of alife simulation complexity $\equiv$ adaptedness, in the real word complexity $\neq$ adaptedness.

Good reading can be found in a link that AMR posted in a comment to another answer.

* Artificial life (alife) simulations of evolution generally use complexity as a proxy for fitness such that selection will be directional for increased complexity

Just as a response to a comment you made under your question, as to why simulations don't produce "stylized facts found in real evolution": Scientists understand quite well how evolution works (as explained in my answer, is a result of selection, genetic (co)variation, and population demographics), however, simulation to produce "stylized facts found in real evolution" would require a complete and precise history of the selection, genetic (co)variation, and population demographics that have existed since the origins of life. That is why simulation does not work like you think it should.

Concerning your edit: No, no, I am not talking about an exact reproduction of how evolution worked out on earth but about stylized facts, that is e.g. emergence of real innovations. And it is not about me but the whole Alife community - please see also the links given in the new edits here: http://biology.stackexchange.com/questions/42033/what-are-we-missing-about-the-real-workings-of-the-evolutionary-process

"I must explain why I have a general feeling of unease when contemplating complex systems dynamics. Its devotees are practicing fact-free science. A fact for them is, at best, the output of a computer simulation: it is rarely a fact about the world... My difficulty, then, is that I do not know what observations complex systems dynamics is trying to explain. It is a theory looking for a question to answer." - John Maynard Smith.

Could you explain what you mean by "stylized facts found in real evolution" if you don't mean similarity between simulated and real evolution? @vonjd

@AMR: something for you to read: appeal to authority fallacy: https://en.wikipedia.org/wiki/Argument_from_authority

"stylized facts" is a term mainly used in QuantFinance and I'll come back to you to clarify what I mean by that. I have to run now to deliver a lecture... till then...

@AMR... by the way, the quote continues: "the only time a fact was mentioned was when I mentioned it, and that was considered to be in rather bad taste." - so he was the smartest guy in the room surrounded by idiots... by his own account... why am I not impressed?

@vonjd John Maynard Smith was often the smartest guy in the room...

I am now reading through your answer again. So you are saying the evolution and adaption are two separate things and adaption is the process by which improvement comes into the world?

@Vonjd In a sense... Separate, but not separate... Adaptation is kind of a "sub-process" of evolution... It's important to the question - you want to know why evolution is not about improvement - therefore you need to know what evolution is about (change). Its much the same way that all humans are mammals, but not all mammals are humans. Adaptation is an evolutionary process but evolution is not only adaptive. It is because of selection that we get adaptation on such a grand scale.

Ok, so that would mean that we are missing something in this sub-process of adaption. That there are some details that we have not understood well enough yet to be able to simulate them.

No, you could simulate adaptation icluding the effecs of mutation, migration, drift, and genetic covariance - we understand the effects they have on adaptation very well - you'd just get something that is not realistic because those processes are complex, variable (spatially and temporally) and, most importantly, quite random

It might help to not think about evolution as a process at all - it tends to imply some sort of planning or goals or something like that. That's not what evolution is - evolution is simply a fact. When we talk about "the evolution of humans", we're describing the history of various human precursors. Evolution is basically a historical record of things that worked in the past in a given environment.
Most people tend to antropomorphize evolution, give it goals. There's no such thing, and it just makes you even more confused. There's nothing paradoxical about "evolving to extinction" - evolution is not a path from a base organism to an improved organism. It's simply a history of the changes that survived and thrived in a population. Sometimes that's because those changes gave the individuals and populations a better chance of surviving in their environment, so those traits became more and more prevalent in a population - for example, skin turning to hardened skin, turning to armour plates or weapons, or a better beak allowing it to reach into a food source that isn't available to others. Sometimes, it's simply dumb luck - don't forget that there was a point where the whole (pre-)human population was reduced to a ridiculously low number (I think it was something like 10 000 individuals or maybe even less). It would only take one local catastrophe to kill off the whole human species, no matter how "improved" and "advanced" we might consider ourselves to be.
Another rather brutal example would be the evolution of photosynthesis - when the atmosphere started filling up with free oxygen, it killed off almost all life on the Earth. Sounds like an improvement? Getting rid of your competiton? Well, it also fueled a massive growth of new species that were not only adapted to an oxygen atmosphere, they used it as a source of energy! Not only would they thrive on the "waste products" of the photosynthesers, they even consumed them.
Even if you wanted to describe evolution as a process that improves fitness, you must not forget that a change that improves your reproduction rates in one kind of environment can hinder (or kill) you in another.
When pre-Koala bears drifted to be exclusive Eucalyptus-vores, it gave them an advantage - they had a food source noöne else can use. But it also made them 100% dependent on Eucalyptus. When Eucalyptus dies, they will as well. Something that was arguably an improvement can easily be the thing that kills of your entire species. It only "improved" their ability to survive and thrive in one specific environment - it also entirely locked them in their niche.
In summary:

Evolution doesn't have goals, so it's weird to say "evolution is about improvement". Random changes have a tiny chance of becoming (locally) useful traits, and useful traits have a tiny chance of becoming entrenched in the population, and thus forming a new species over time. It's a history of changes, not a prediction of the future. The great thing about Darwin's Theory of Evolution is that it predicts what kinds of changes are possible (and which are impossible!) - for example, that complex systems can't arise out of the blue, or that different branches of history ("evolutionary tree") cannot exchange traits.
Almost all changes also have their drawbacks - it's a balancing act. There's some great examples of changes that are almost universally good - sexual reproduction and human-level intelligence are a great example of something that works in almost any environment. But even so, there's still examples of where they didn't "win" yet. There's still asexual reproduction on Earth, and most of Earthly life doesn't have human-level intelligence yet. Lions do not rule the world, even though they're apex predators in some environments.

Thank you - are there also instances where species "devolved" into less and less complex ones over long periods of time? Because it seems that they normally evolve into ever and ever more complex ones or they stay the stable or they die out (like in big consulting firms: "Up or out" ;-)

@vonjd It happens all the time. The Koalas are one example. Cave fish evolved from normal fish, but they lost their pigmentation and sight - "became less complex". Species change their diets all the time (e.g. omnivore -> herbivore -> carnivore -> ...). That's why "devolve" doesn't make any sense - there's simply changes. Are there examples of a multi-cellular organism becoming able to work as a single-cell organism again? In modern history, probably not (though you might count "colony" cells). What do you really mean by "complexity"? Are rats more complex than fish?

@vonjd If you look at genetics, for example, humans are one example - on of our chromosomes is damaged, and we "lost" others. All Mammals have two-colour vision by default, because some common mamallian precursor lost two colours - once you go far away, tetrachromancy was the standard. Some mammals got one colour "back" (including humans). We no longer have a tail, and our nails are almost nonexistent. Our intestines got comparatively shorter over time. Does this mean we're "less complex" than primates or other mammals? Is a plant with 100 chromosomes more complex?

I gave an example of complexity in my question: "e.g. in the capability to process information and the sophistication of the internal representation of the world." - Are there examples where species got "dumber and dumber" over long periods of time? I am not talking about skin colour more or less dark.

@vonjd I just gave you one - cave fish (and other cave dwellers) lost sight. The thing is, "in the capability to process information and the sophistication of the internal representation of the world" is a gigantic topic we can't really quantify well. If we could, we'd already have (or be on the road to) "real" artificial intelligence - we don't know what makes intelligence "tick", even, say, dog-level intelligence. We know tiny subsets (mostly the kinds where there's a direct A -> B relationship, like reflex movement).

@vonjd That said, complexity generally tends to increase. It's hard to keep complexity low, not to increase - something every programmer can tell you from experience. Hell, an accountant can tell you the same thing! Keeping things simple while still having them work is a huge challenge. It's actually far more impressive that evolution can simplify things - complexity is the natural thing. And if you look at human DNA, a huge portion of it is just pure garbage, like remnants of old, mis-transcribed viruses and such.

"It's hard to keep complexity low" - and yet it happens in Alife simulations all the time...there it seems to be impossible to increase complexity over a certain level! Why?

@vonjd Because most of evolution simulations have very big fitness penalties for code size. This is mostly due to two things - one, the larger the code and complexity, the slower the simulation, and two, you usually use evolutionary algorithms to try to find a cheap solution to a problem. If you remove the complexity penalty, complexity is going to explode all over the place. In nature, there are still costs to complexity (it takes energy, resources, ...), so it isn't unbounded either, but it's a lot less strict, since it's usually relatively small compared to e.g. the whole body.

So you are saying that if we were to allow for larger code sizes in those simulations (by reducing the associated costs) we would see more improvements (in the sense of e.g. higher intelligence) in those creatures?

@vonjd No, just a higher complexity. The thing is, to get intelligence, you also need some pruning mechanism that gets rid of the things that don't work. Now, evolution is such a mechanism - but that's where it gets absurdly computationally expensive. If we want to have a chance of "evolving intelligence" artificially, we must guide the evolution far more than the natural evolution does. It's a problem of big numbers, basically - you're trying to simulate something that took billions of years running on the whole planet in a few days on a single computer. That's not easy.

@vonjd So the real answer is more like "Yes, eventually. But don't hold your breath - if you're lucky, it might happen in a few billion years. Or a few hundred billion. Or the universe might end before it happens." Your computer is going to die long before you get anything useful.

Ok, so you are saying the biggest obstacle is still computing power (and storage size)?

@vonjd I'm not saying waiting for more computing power and storage size is a good strategy. We need to figure out how intelligence works and find a way to reproduce it - I don't think we necessarily need more computing power and storage than we already have to run an AI; it just isn't enough to semi-randomly create it. But yes, if you had unbounded storage space and computing power, you'd get to intelligence eventually. The universe is fundamentally very simple, and it produced intelligence anyway - it just took a huge amount of work, and lot of time.

Why have the answers either side of this been tagged for improvement by adding references, and this not? It makes similar sorts of claims without offering citations.

I'm going to chime in here. As both a scientist and a software engineer.
Firstly, evolution is not about improvement at all. It is about survival and random change. There are as many if not more mutations that are disadvantageous. But they tend not to survive.
On the other hand, genetic algorithms are an attempt to use a similar process of mutation and survival of the fittest.
But the first step in a genetic algorithm is to define a fitness function. This function will cull the weakest algorithms, just like an environment kills life in the real world.
A good primer on Genetic Algo can be found on https://www.youtube.com/watch?v=qv6UVOQ0F44
However that fitness function will only optimise for certain goals. For example, a badly tuned fitness function will end life on earth either by the paperclip apocalypse, or giving rise to skynet.
In these cases the algo is not improving towards the goals you want. But never the less it improves.
Another complexity is that, genetics is a very greedy optimisation strategy. Mutations tend to be small, because large mutations tend to more often move away from optimal solutions. This means that evolution can only find local maximas and will often miss the global maxima.
Hence improvements can only occur when there is a small tunneling cost to the new maxima.
An example of this can be found in mammalian eyes. Our optic nerve passes through retina and connects to the front of our retina, and physically blocking the retina from doing an optimal job. If evolution were able to find a global maxima, then mammals would have been able to evolve to have squid like eyes, which route from behind.
Moreover, had evolution been about pure improvement, then we should have evolved away our blind spot many many generations ago.
However, human ancestors have rarely been attacked by circles and crosses that are precisely spaced apart in the African continent.
Saying that evolution is about improvements is like setting up a school where there is no teaching, and every year you expell the bottom 10% of the students.

If your answer contains no scientific claims/statements how can it possibly serve as an answer on a biology Q&A site?

@rg255 Logic. The first half of the post merely reiterates definitions. The later half saree the consequences. I'd admit that I have no basis for the statement of no quantum leap mutations a la x men.

You cannot compare GA with actual process of evolution because in reality the fitness function keeps changing. In reality fitness cannot be assessed unless selection happens.

Sorry, You are saying that inter-species competition does not cause for simpler organisms to become prey and be vulnerable to extinction. Evolution caused complex functions of motility and defense to be added to every species on earth, even plants all have biochemical and physical defense, and simpler organisms that exist in extreme environments use the environment as a defense, without which their simplicity would cause them to be decimated.

@comprehensible Correlation is not causation. Complexity is only one of many solutions to the ever changing problem.

Evolution produces branching trees, and branching is multiplicity.
Fitness is associated with complexity, and, with radiation into more or less challenging environments. Fitness increases with versatility and added functions, in equilibrium within one niche, and for change across niches. Are cold blooded animals simpler than warm blooded ones? the consensus is that they are simpler, less apt and globally superseded. Even if a lizard has as many genes as a human (40,000) it is less complex than a human.
Take Motility for example. Locomotion is complex, compared to passive or sedentary displacement. Most prokariotes have evolved some kind of motility, cilia and flagella. The simpler procariotes were eaten out of existence, as the motile ones grew to dominate and pervade. There is a predator prey issue which has resulted in the extinction of simpler, slower species and in the promotion of more complex ones. The ones that survived did so by adding defense functions.
Life is thought to have started in simpler environments with less biochemical and physical fluctuations than it later evolved into. Eukaryotes have not evolved back into prokaryotes, even though they could, and eukaryotes have more scope for complexity, same as lego blocks in multiple numbers are not as simple as single blocks.
Evolution is also about the blind use of an initially small but potentially much larger memory bank of many gigabytes."Gene Duplication is believed to play a major role in Evolution."
Unless life began in greater quantity than it now exists, evolution requires that natural processes have, over time, increased the total quantity of genetic material (DNA) present on our planet.
I'm going out on a pirate's plank of logic here. sorry about that.
Survival fitness is about increased complexity when the environment is increasingly complex. Evolution Causes complexity to occur...
Change is an additive process, and the more change is provoked, the more added functions tend to result along.
The more complex the path has been to arrive at the current stage of the species, the additive complexity rises. (Also the DNA keeps on record the genes from old environments to not lose many precious years spent finding useful genes/biochemistries, while adding new ones). However evolution can be about the conquest of less complex environments:
Put a fish into a cave with no lights, constant temperature, and simple tasks, it will lose some of it's complex genes and may, over time become genetically simpler, than a fish living in a river. It requires less senses, less thermal adaptations, less locomotion pressure and less species competition. It is rare for species to retrograde generally, they tend to extend their range, but in deep sea and caves, locomotive and biochemical retrograde can happen.
Increased size gives increased fitness in most settings: larger metabolic reserves, less sensitivity to change, bigger predator pray advantage... and bigger size means more cells, different locomotion pressure, which means different metabilic resource distribution(lymph, digestion, blood), and That is that's the essence of your complex topic: Do environments encourage complexity? if so, how "complex" are environments in the universes land/sea biomes? Geology, climate and hydrology are of incredible complexity... So... can we say evolution is not about the conquest of new environments? It requires a good philospher to shed light on this question.
The pressure is more often on increased performance in a complex environment using highly complex foods and locomotions.
Increased complexity is an inevitable ramification of the evolutionary process through time and space, rather than a direct and inevitable requirement of it.
Because species evolve into new niches, The most logical and efficient way to do that, is to keep the genes for old niches, in the DNA library, and to add new ones next to it. If the organism did not keep genes from old niches, and use them for a proportion of it's mutations, it would be less apt. Useful genes are costly, they can cost millions of years to find them, for example The more of a toolbox of biochemistry and morphology.
For Biochemistry, life "discovers" new materials and proteins, and puts them to use, and it keeps a record of those materials after they are not needed.
A sea slug can evolve into a vertebrate fish, but a fish can't evolve back to a slug, because complexity enhances fitness, so perhaps we can say that fitness and complexity are not disassociable.
Change is complex thing, and evolution is about changing, so for me, evolution adds complexity every time it changes.

You need some citations here, especially because your answer is somewhat at odds with existing answers. It's also quite difficult to read.

Except life very often evolves to be simpler, vestigial and lost structures are a great example. complexity can enhance fitness it can also reduce fitness.

The simplest way to look at it is there is a near infinite number of ways to be more complex but a very limited number of ways to be simpler. There is even fewer ways of being simple. So even with just pure random variation, over time all things being equal you will end up with more complex organisms.
This becomes even more true when you take competitor into account, competition, go too simple and you lose the ability to do things that you really need to compete with the rest of life around you, go too simple and you can't reproduce fast enough to keep up with all the things eating you. While on the other hand the cost of complexity can be offset by better capabilities. On top of this imagine a ruler with as simple as life can be and still function on one side and as complex as life can get and still function on the other. the first life is going to be pretty close to the simplest possible life, so most of the ways of being alive that are possible are going to be more complex, so again even if you ignore selective pressures either way, just random variation is going to create more complex life than simple life. There is more complex phase space to occupy than simple phase space.
imagine I stand with my back to a cliff and throw a ball randomly in the air, now after I throw a thousand balls the vast number of balls I find are going to be in front of me, not becasue I am actively trying to throw them there but because most of the balls that fall behind be get lost, (go extinct for our analogy)

"The simplest way to look at it is there is a near infinite number of ways to be more complex but a very limited number of ways to be simpler. " I think this argument is flawed: it would mean that in artificial life simulations you would see the same tendency towards enhanced complexitiy... which is false. The agents in those simulations tend to stay on the same level of complexity.

@vonjd I will admit simulating life is not my forte, but simulations of evolution do often show an increase in complexity, that is not to say only more complex life evolves but that on average there are more complex entities than your starting condition. Of course the actual history of life also shows this.  I would argue not seeing increasing complexity in a simulation is evidence of the limitations of the simulation since it is not matching the real world system it is trying to simulate.

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What are we missing about the real workings of the evolutionary process?
As a scientist (and a computer scientist at that) my view is that if we cannot simulate a process we have not understood it properly. I have been following the interesting field of Artificial Life for quite some time and the results are sobering - let me just quote two paragraphs from current overview articles:

One thing that always seems to happen with such projects is that,
  after they achieve their intended aim, if the ‘evolutionary’ program
  is allowed to run further it produces no further improvements. This is
  exactly what would happen if all the knowledge in the successful robot
  had actually come from the programmer [...]
That is why I doubt that any ‘artificial evolution’ has ever created
  knowledge. I have the same view, for the same reasons, about the
  slightly different kind of ‘artificial evolution’ that tries to evolve
  simulated organisms in a virtual environment, and the kind that pits
  different virtual species against each other.

Source: David Deutsch (2011): The Beginning of Infinity

One of the earliest networked artificial life experiments was based on
  the well-known A-Life system, Tierra. This was created in the early
  1990s by the ecologist Tom Ray to simulate in silico the basic
  processes of evolutionary and ecological dynamics. After Ray began his
  work, he soon recognized the potential of the Web to create a large
  complex environment in which digital organisms could freely evolve. So
  he set up a project called Network Tierra to exploit this potential
The results of this experiment were mixed. One goal of Network Tierra
  was to reproduce the Cambrian explosion in which single-celled
  organisms on Earth evolved rapidly into multicellular ones and then
  into more complex animals.
The in silico experiment began with a human-designed multicellular
  organism consisting of two different cell types. This survived under
  natural selection, a significant success in itself, but the number of
  cell types never increased beyond two.

Source: MIT Technology Review (2014): The Curious Evolution of Artificial Life
The point is that I have myself successfully worked a lot with genetic algorithms and genetic programming (I am also teaching this stuff) but what bothers me is that we are still not able to create some abstract form of (co-)evolution inside a computer where some real dynamics take place to produce ever and ever more sophisticated "species".
My question
Are there hints from the biological sciences what this mysterious ingredient could be which we still seem to be missing? Is it physics? Is it chemistry? Is it something else?
EDIT
Obviously the question is not clear as it stands, so I try a clarification: I refer to complexity of the resulting "species" in artificial life simulations. For example their behavioural or structural complexity. Why do these simulations always get stuck at some very low level (e.g. following food) and never ever even create something as complex as a bacterium? The computing power should be more than sufficient by now - and still, nothing... It seems that only what has been put into the simulation comes out but real evolution produces something really new (this is what the renowned scientist and polymath David Deutsch (University of Oxford) means by "I doubt that any 'artificial evolution' has ever created knowledge.")
EDIT2
Nathaniel gave me a decisive hint in the comments that this problem is called "open-ended evolution (OEE)" in the Alife community and it is one of the biggest research challenges there - unsolved yet! As a starting point see here: https://www.google.de/search?q=%22open-ended+evolution%22&artificial&life
Very interesting that it doesn't seem to bother the biological community and is met even with hostility here (some even lecturing me that the evidence for evolution is overwhelming and thereby implying that I might be some kind of crackpot creationist - unbelievable...)
...and no, the answer is not a matter of opinion (why this question was closed) but a valid research question (hopefully with some good answers someday)!
EDIT3
Last year there was even a big conference on this topic with many interesting results (although the problem itself is still unsolved):
http://www.tim-taylor.com/oee1/

See also my follow-up question here:
If evolution is not about increased complexity, why does so much complexity evolve?

@Downvoter: It is good practice to state your reasons. How can I improve the question? Thank you.

I am voting to close as this question will only generate answers based on personal opinion. I will also be the second downvoter, as we understand and have 150 years worth of scientific evidence to support Evolution and the process of Natural Selection. Furthermore you have cherry-picked two articles that back up your point, and it is not the case that "if we cannot simulate a process we have not understood it properly." Galileo could not simulate the solar system, yet he could prove heliocentrism. Newton and Leibniz could not simulate the infinitesimal, but they could understand it.

@AMR: What does it have to do with it that we have evidence of evolution with my statement that we don't really understand the process? We have evidence of qualia and yet don't understand it scientifically. We have evidence of quantum mechanics and of the general theory of relativity and are still not able to combine them - so what is your point? And I would argue that Galileo, Newton and Leibniz didn't know all there is to know - and today we are able to simulate those processes!

@AMR:  If you think I cherrypicked something give me one reference (only one) where artificial simulation really created something sophisticated.

http://jgs.lyellcollection.org/content/147/2/223.short

@AMR: Are you kidding me? I had a quick look at the paper and I have done much more sophisticated stuff with genetic algorithms myself. The author only writes about some simple simulation on a simple morphological structure - which is not surprising considering the paper is over a quarter of a century old. Seriously: You should acquaint yourself with the area of artificial life which is much richer and deeper. The wikipedia link I gave above is a good starting point.... and yet it does not fly! (and no, it is not a matter of opinion but a hard scientific question!)

@AMR: See my edit at the bottom of the question - Thank you

You've answered the question in your first lines: .. my view is that if we cannot simulate a process we have not understood it properly. How can you make a model whilst it is unknown how the first cells appeared and why and how multicellular organisms arose? It ain't magic; if you don't put the parameters in, a model won't generate magic

There is an inherent problem to Deutsch's argument, " if the ‘evolutionary’ program is allowed to run further it produces no further improvements." He is falling for one of the biggest misconceptions about evolution that you can, that evolution is about "improvement." Evolution has simply only ever been about change. Think of sickle cell anemia. Without Plasmodium driving selection, sca is a debilitating disease, and the wild-type population thrives, while scan mutants are less fit. No throw in the parasite, and people with the mutant allele survive while WT organisms die.

Under one set of conditions one group is more fit, under the other, the other group is. Neither is an improvement or better, they are what they are in the context of the environment that they find themselves in.

@AMR: Your last two comments were quite helpful - Thank you.

@AMR: Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

@Christiaan: Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

@shigeta: You write in your profile: "I believe that biology and other stackexchange sites should welcome newbies and encourage dialog about science, evolution, technology and etc." Just to let you know I don't feel very welcome here (just look at the comments) and I know how stackexchange works... see e.g. my profile here: http://quant.stackexchange.com/users - I know it is not your fault but it is a shame for the biology.SE site. Sorry to say...

This is known in Artificial Life circles as "the problem of open-ended evolution." It is (still) an area of active research (e.g. there was a workshop on it at the last ALife conference in York, UK last year) but nobody believes with certainty that they know the answer at this stage.

In my opinion it's mostly due to the constraints of computing power, which was very limited throughout most of the field's history, and is even now not quite enough. Because of this, we tend to use the smallest population we can get away with, and very strong selection pressure, because otherwise we would be waiting too long for anything to happen at all. But with small populations and strong selection you should not really expect anything other than strong convergence to a local optimum.

Deutsch's "doubt that any 'artificial evolution' has ever created knowledge" seems unfounded to me, since there are a moderate number of well-documented examples of exactly that, in the form of electric circuits that work in ways no human ever designed, and that sort of thing. (But I have not read Deutsch's paper and perhaps he addresses those.)

@Nathaniel: "the problem of open-ended evolution." - this is a very good hint from where I can get started. Interesting though that the biological community seems unaware of it...

@AMR: See my new edit2 - perhaps you would like to reconsider your downvote... Thank you

No. Nothing has changed. I do not need a computer simulation to be able to look at genetic homology between species and understand how evolution worked. I can look at actual data and interpret it. It tells me why humans have 46 chromosomes per diploid cell and not 48 as our ape relatives have. I can look at fossil records and see how species evolve. The Earth has provided all of the simulation and data for evolution and natural selection that one would ever need to understand it and prove it.

You are also missing the point of the close votes. None of the answers on your other post are referenced and they are most all based on opinion, even if some of them are valid opinions. The guiding principle of the site is not to allow for questions that lead to that kind of answer. It is for questions that will lead to fact based, referenced answers. Your question is more appropriate for a lab meeting or a working group to brainstorm ideas and not for a site that does not look to be an open opinion forum. There are plenty of those on the Web and that is not what we are trying to be,

@AMR: You don't get the difference between "seeing that something works" and "understanding how something works", right? For you it is enough to stand in front of a miraculous machine in awe and just acknowledge that it works. We agree on that one. What seems to be the difference between us: I really want to understand how it works - up to the tiniest detail of the process. This is why I became a scientist and this is why I asked this question: "What are we missing about the real workings of the evolutionary process?"

Again, you are pretty much completely wrong and that is likely because you haven't truly invested the time and effort to learn biology. It isn't mysterious or miraculous. It is predictable and behaves in a logical way. That is why it is the Theory of Evolution and not the hypothesis of evolution. We understand Evolution so well that we can use it to make predictions about nature and natural phenomena that hold up and can be proven to the greatest rigor of science. The only things we are missing is actual proof of the very beginnings up to the Last Universal Common Ancestor.

@AMR: Oh, and by the way I am fully aware of how SE sites work - I am No. 1 by far on one of them: http://quant.stackexchange.com/users - and to tell you the truth: The climate is much more welcoming there than here!

To clarify my close vote - you write ...and no, the answer is not a matter of opinion (why this question was closed) but a valid research question (hopefully with some good answers someday)!... It may be a valid question, but the answers will be primarily opinion-based, making the question off-topic here.

@AMR: "The only things we are missing is actual proof of the very beginnings up to the Last Universal Common Ancestor." You forgot the point that we are not able to reproduce open-ended evolution in a computer simulation... So there are at least two things...

@Christiaan: How do you know that before you have even encountered the right term under which the research results are to be found ("problem of open-ended evolution OEE")???

A computer cannot reproduce Graham's Number, even though it is finite, and has been used in a rigorous mathematical proof. So I am not going to lose any sleep over the inability of computer "scientists" to simulate evolution in silico. Computers are a great tool, but just because something cannot be done with them doesn't mean there is a problem with the actual event they are trying to mimic.

@AMR: Fair enough, yet I think the problem is not that we are not able to reproduce it but that we do not even know what is missing (we know that in the case of Ramsey theory!) - and this is all I asked for.

I may be entirely wrong, but I don't hear Evolutionary or Paleo Biologist saying that we need computer simulations to understand the subject, in the same way that astronomers ask for computer models that simulate cosmic expansion or galaxy formation. And that is because the model we are given from the Theory is fairly complete. We may have gaps, but we have no unexplained life forms to date. I can plate a dish of yeast, mutagenize them, and put them under selection, and I will, in some small probability find adapted mutants not present in the control. And we understand the mechanism for that.

""What are we missing about the real workings of the evolutionary process?" - "Are there hints from the biological sciences what this mysterious ingredient could be which we still seem to be missing? Is it physics? Is it chemistry? Is it something else?" - Different people see "different hints", and there is no definitive answer. Anybody could be correct as to what direction we should head in. This is the very definition of opinion based. That doesn't make it a bad question - it just means there cannot be "one true answer", which is what the SE sites aim for, (as you know)

@AMR "I may be entirely wrong, but I don't hear Evolutionary or Paleo Biologist saying that we need computer simulations to understand the subject" - actually there are quite a number of very senior and influential evolutionary theorists who use simulation extensively. Szathmáry and Nowak both come to mind straight away, though there are many others.

@Nathaniel Szathmáry is using simulation to test specific hypothesis about molecular machines and also multicellular development, based on the assumption that evolution does occur in a predictable and understandable way. This question is saying that we cannot understand evolution as a mechanism if we cannot simulate it in a computer, there is a difference. "if we cannot simulate a process we have not understood it properly." That is very different from using a computer algorithm to test a hypothesis that would have developed over millions or billions of years.

@AMR: Imagine physicists observe the solar system and collect evidence beyond a reasonable doubt that the planets are attracted to each other and that "gravity" is responsible for their movements. They even summarize "gravity" into some nice laws. Now you put these laws into a computer and see how different objects are affected. Unfortunately instead of ellipses the objects move in rectangles. Getting back to the physicists they tell you: "Never mind, we fully understand gravity and we don't have to simulate it because there is evidence that the planets move in ellipses" How would you react?

The sim is wrong, not the evidence or the theory. Take a look at the code you wrote and find the error. You are enamored with computers and for some reason cannot seem to divorce yourself from the idea that the data that they generate is perfect and that everyone else is wrong. If you want to deal in hyperbole, if a simulation has sheep giving birth to monkeys, I'll find the grad student that wrote the code and tell them to kindly stop being sophomoric. If however, an entire flock of sheep start giving birth to monkeys, then I would say that maybe we have to look at something in evolution.

@AMR: Very good answer indeed: many scientists double checked the code over decades... still rectangles (see my edits and the links given there). I am not saying that the theory is wrong but that it may be incomplete (you do recognize the word "missing" in my question, right? And you do understand that this is different from "wrong"?) And one clarification: Declaring oneself to be beyond any criticism whatsoever is not only bad style but is not science either. The majority of this community agrees with me now so you seem to be the one saying "everyone else is wrong" ;-)

"The majority of this community agrees with me now...." Because you have a question that got 21 upvotes? @Remi.b got 39 upvotes for an answer about Morning Wood

@AMR: So obviously this is also a great contribution - but I did this in one (!) day and that answer is nearly two years old. I am very happy about this development :-)

Does it not phase you that almost ever single answer tells you your premise is basically wrong? And he got 78 for this answer, even though he didn't bother to provide references and the original, unedited question got 26 upvotes, even though its premise was entirely incorrect. http://biology.stackexchange.com/a/40581/16651

I'm also a CS guy, so maybe my opinion shouldn't have that much weight, but I did do biology research in college and I work at a company that produces biology research instruments. That said: I find it pretty sad that this question was closed, and it's absolutely ridiculous that it seems to have been interpreted as "in your opinion, why is the theory of evolution flawed/lacking." (Not to mention the bizarre comparison to "simulating" infinitesimals, whatever that means.) Have an upvote.

@KyleStrand the close is not about the question. "Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise."

@AMR: My premise is that we are not able to successfully reproduce important stylized facts of evolution and this is correct - many people agree with me. But I understand that you think that you are the one in this community who decides which premises are correct or incorrect, you are not to be bothered by dozens of votes from the community. I think you should just relax and find a good answer to my question - Thank you.

@AMR I see that. I don't understand why the question as stated is necessarily opinion-based, though.

@KyleStrand It does not matter if the question is opinion-based or not, what matters is the answers that are generated. And as can be seen from the follow up to this question one out of the ten answers had a reference. The majority of posters opened accounts yesterday to answer, and as such, that goes against the stated goals of the site.

@AMR: So questions that are able to grow this site are against the stated goals.. I think you might have misunderstood something here ;-)

It is actually the stated goal of the entire network. http://blog.stackoverflow.com/2010/09/good-subjective-bad-subjective/

@AMR The frick?? No. No no no no no no no no no. You do not close questions because bad answers show up; you delete the answers. You do close questions that have an inherent tendency to attract opion-based answers, i.e., subjective questions.

@AMR In case the above gets flagged as rude (sorry, but it shocks me that someone with a relatively high rep on any SE site would so badly misinterpret the subjective-questions policy), I'll restate the point more calmly: the blog post you cite clearly states that it is about questions. The fact that a question draws some opinion-based answers may be an indication that the question itself is opinion-based, but it is not in itself sufficient reason to close the question as "primarily opinion-based."

@AMR: Please show us the part where it says that it is "against the stated goals of the network" when "the majority of posters opens new accounts to answer". I think this is getting more and more ridiculous and we can all see that you are just not prepared to admit a mistake.

@vonjd Extensive discussion in the answers or below in the comments is indeed discouraged. stackexchange is set up as a question and answer site, not as a discussion site. This is something you can regret in terms of scientific discussions, but these are the rules. You could look into this stackoverflow post for example. Opinion-based questions are seen as unscientifically and not welcomed here on biology. So please, respect the rules of the community. And please stop discussing in the comments and move this over to chat.

If the discussion goes on here, I will move it.

@Chris: I fully agree and upvoted your comments. Thank you for your service to the community.

I don't really understand @Chris's objections to the discussion here; AFAIK there is no problem with using the comments to discuss whether a question is appropriate for a site, though at this point perhaps someone should just open up a Meta question to resolve the issue.

But OP, I think am beginning to understand the claim that your question is subjective, and I think it could use a pretty substantial edit (not just another addendum, but a rewrite of what's already there) to clarify who "we" are when you say "we are missing" things in the theory of evolution. As far as I can tell, there are two possible interpretations, and both are actually fairly valid as (non-subjective) questions:

first, "what parts of evolutionary biology are missing from CS evolutionary simulations?" (which would completely evade the "evolution is not in doubt" argument from @AMR et al), and second, "what open questions in evolutionary biology might also be 'missing factors' in CS simulations of evolution"? The second question is obviously more open to the charge of subjectivity, though I personally wouldn't consider it overly subjective (though it might be too broad; every field, including reasonably well-established ones like evolutionary biology, have lots of open questions!).

@KyleStrand: I agree but I am pondering the possibility to ask a new and clearer question. New because closed questions tend not to be reopened on SE sites and I want to give everybody a chance to answer this question.

Editing is nevertheless the "standard" way to go. Note also that editing a closed question automatically places it in a "reopen" review queue, so there is actually a good chance it will be re-opened if it has improved. (There may be some exceptions to the automatic-placement-in-the-reopen-queue rule, but I'm not sure.)

@KyleStrand The comments are not meant for discussion here on stackexchange. Like it or not, but respect it.

@Chris When the automatic "continue this discussion in chat" link is created, I almost always use it, but there apparently has not yet been enough back and forth to generate one. Since you have the mod-powers necessary to move the discussion without the auto-gen link, it would be fine if you did so (your earlier comment to that effect sounded a bit like a threat, though, which I found odd).

Based on prior experience, I don't think your attitude is actually all that standard for StackExchange; between this and OP's follow-up question (where non-discussion comments were deleted), it seems Bio.SE is generally less welcoming toward long sequences of comments on a question. This is fine, but I'd appreciate it if you stopped accusing the rest of us of not understanding network-wide policies.

@KyleStrand: I asked the new question and it is again on the brink of being closed although this time I really tried to make the point explicit: http://biology.stackexchange.com/questions/42151/bootstrapping-symmetry-breaking-in-evolution

@AMR : Galileo is a bad example, because he didn't prove heliocentrism, he was right for the wrong reason. He argued that the tides are caused by the motion of the Earth around the Sun, while his opponents correctly argued that the tides are caused by the Moon. Also, the theory of gravity was not invented yet, so as strange as it seems, with the instruments available at that time, heliocentrism didn't have any more solid scientific proof than geocentrism.

@vonjd If you are interested, I recommend looking into Basener's ceiling.  It's a mathematical proof that all evolutionary algorithms hit the complexity limit that you see in practice.  It is from the Intelligent Design literature, which I know is disliked around here, but the proof is sound.

The question appears interesting and made me think but I might not fully understand it. Let me know if I am answering your question.
Genetic algorithm vs simulation of evolutionary processes
I think that the whole issue comes from a confusion between the concept of simulating evolutionary processes and the use of genetic algorithm (type of optimization algorithm) for various purposes.
Genetic algorithm
Genetic algorithm is a type of optimization algorithm (and the OP knows much more than I do in this field) aiming to find solutions to search problems. The accuracy of the analogy between a genetic algorithm and the biological reality that inspired such algorithm is completely irrelevant to the usefulness of the algorithm at doing a specific task (such as the NP-hard travelling salesman problem for example).
Numerical simulations in science
I think your question is not specific to evolutionary biology but rather to science as a whole (this leads me to think that Philosophy.SE would be a good place to ask such question).
In natural sciences (Physics, Chemistry, Biology and others), we model things! We abstract the essentials from a complicated world and model it. When we model, we assume a number of properties of the system of interest. These assumptions might be extremely well documented and verified or not. When the assumptions of a model are not well documented, it is of course essential to study a posteriori the robustness of the model to violation of the assumptions and to consider the results of the model with a pinch of salt. A model can be purely verbal or most often expressed in mathematical formulations. However, many complex systems cannot be modelled mathematically (even for the most brilliant mathematicians). This is where numerical simulations come into play. Note that once a process has been modelled, we empirically investigate the accuracy of our model by formulating predictions and testing them.
You say:

if we cannot simulate a process, we have not understood it properly

If we already understood a process, there is no point spending time and money to simulate it anyway! So again, this sentence suggests that numerical simulations is worthless in science. It is true though that we can only simulate the processes for which we know the basic components (but we might not understand the dynamic of a system of interest).
Simulations in Evolutionary biology
You cite one work (which I am not familiar with) which fail to reproduce the observed pattern. In other words, the predictions of the model are not met/observed in reality.
As I said above, one needs to understand the basic components of a system in order to be able to simulate it. We happen to already know a faire amount of stuff! Of course, it is impossible to address the question "what do we know in Biology" as it would be way too broad. There are thousands of studies that have used numerical simulations (and also mathematical simulations) to study evolutionary processes.
Example
Imagine for example, you are interested to know the probability for a given new neutral mutation to rise in frequency in a diploid population to reach "fixation" (that is a frequency of 1; everybody then carry this mutant allele). There exists a number of mathematical models (Wright-Fisher (binomial) model of genetic drift, Moran (Birth-death) model and Coalescence (branching process) model) to calculate this probability but let's assume we fail to develop such mathematical/analytical model and and we need to simulate it. We could simulate this process a lot of time (using a ABC kind of approach) and calculate the expected probability of such mutant allele to get fixed. Btw, this probability is $\frac{1}{2N}$, where $N$ is the effective population size.
Want to know more?
I am not a philosopher of science (but a PhD student using numerical tools to model evolutionary processes) and I think the question is not specific to evolutionary biology. I would recommend to ask the question What is usefulness of numerical modelling in science? or Are numerical modeling worth as much as analytical modelling in science? on Philosophy.SE.
If you do so, can you please link to your posts here, I would love reading the answers. If you don't ask these questions on Philosophy.SE, I probably do it at some point and will add the links here.

Thank you, I think your answer goes definitely into the right direction (and I upvoted it). My main question is why has no artificial simulation ever been able to really create something sophisticated but seems always getting stuck after reaching some low level of complexity?

[...] reaching some level complexity. Do you refer to the complexity of the model?

I refer to complexity of the resulting "species" in those artificial life simulations. For example their behavioural complexity. I mean why do these simulations always get stuck at some very low level and never ever even create something like a bacterium? The computing power should be more than sufficient by now - and still, nothing...

See also my Edit at the bottom of the question. Thank you again.

Oh, so your question is less general as I thought. You are talking about a few quite specific type of simulations apparently. You should clarify the definitions of these simulations. Are the authors of those simulations particularly interested in the evolution of cell cooperation in a multicellular organism or maybe they are interested in the evolution of a complex genetic network... or maybe something else.

I would suggest that you investigate a bit more about what they are trying to build (and from which basic mechanisms) and then ask a more specific question about why these simulations fail to produce what is observed. For the moment, it is unclear what category of simulations you are referring to.

If the term artificial life experiments refers to a specific type of simulation, then your question relatively clear I guess. But I would guess that most people don't know what is meant exactly by artificial life experiments (at least I don't)

Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

Agree with the previous answer.

Are there hints from the biological sciences what this mysterious ingredient could be which we still seem to be missing? Is it physics? Is it chemistry? Is it something else?

The OP already seems to support evolution theory, as anyone with basic biology knowledge would do.
Since he is asking about possible "mysterious ingredient", the question is very likely to be regarding the stimulation of the evolutionary process rather than generic algorithm.
Even more specifically, he wants to stimulate the evolution to know if "Probablity theory" will support evolution theory without any need for the "mysterious ingredient".
As answered above, without fully understanding all the components of the system, it may be difficult to stimulate an evolutionary process.  There is not even need for that.
But if you want to test if complex characteristic can be achieved by chance you can stimulate it easily by some other method.
Develop a program which has "face detection" (from the image)function, and add some other functions such as self replication, forced "mutation", and an enviroment which will select the fittest.  Try super computers where your software will  self-replicate "unlimited" times at a second, and consider yourself successfull when your program gain a newer function such as "sex" detection from the or image after several years (assuming sex detection feature will make the program "fitter" at your enviroment)

"The OP already seems to support evolution theory" - why would anyone doubt it? I just want to fully understand it. Anyway, could you please clarify your last paragraph, how exactly would you go about?

I have read some comments under your question,  and thought that some  people may think you do not support evolution because of lack of stimulation.  I am sorry if i misunderstood this.    Anyway i found your question very interesting and already upvoted it.  I agree with you.

Please see my follow-up question: http://biology.stackexchange.com/questions/42050/if-evolution-is-not-about-improvement-why-is-there-so-much-improvement

@vonjd  nice question. i will comment.  I have some computer programming background i generally share your feelings about improvments

Algorithmic depth and processing power are miniscule compared to the algorithms of life. The 70 common chemical elements have Van-Der-Valls force, flow, dissolution, 70^70 simple combinations, a google number of proteins, The best mathematicicans can't even model a single tree... it takes them months to program diatoms, leaves, and they always fail. trillions of gygabytes database and algorythms that cover many cd's are used in biology, and for the moment we rival them to about a billionth of their numerical depth.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


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Why can good music raise goosebumps?
Listening to really good music right now, I was wondering why it raises goosebumps.
Is there any physiological reason for this reaction to specific wavelengths or something?
EDIT:
Due sometimes strong vibration, roadwork for example,  raise goosebumps too I reckon it's about the wavelengths but if someone could explain what is happening and why, that would be great.
CROSS-POSTED

I hope this is the correct SE, music.SE is not i guess.

It may be on-topic here, but Cognitive Sciences.SE may also be worth considering - feel free to cross-post.

Chris makes a good point here but I do like this question. Check this information out seems like this is an active field of study (ie how it results in a sympathetic response).  http://www.exploratorium.edu/music/questions/goosebumps.html

Just read this...  http://www.nature.com/neuro/journal/v14/n2/abs/nn.2726.html

It's a phenomenon called Frisson.  From the wikipedia page.

Frisson (French for 'shiver') is a sensation somewhat like shivering, usually caused by stimuli. It is typically expressed as an overwhelming emotional response combined with piloerection (goosebumps). Stimuli that produce a response are specific to the individual.
Frisson is of short duration, usually no more than 4–5 seconds, usually pleasurable.1 Typical stimuli include loud passages of music and passages that violate some level of musical expectation.[2]

There is some interesting research that shows it occurs when a sound contains certain chords or has certain notes because of how our biological setup reacts to those specific notes.  See the "crying chords" that were supposedly present in Adele's song "Someone Like You". npr article about crying chords

This is an emotional response which elicits a physical response. All emotions stimulate the hypothalamus, a part of the brain, which controls responses such as goose bumps, increased heart rate, and sweating. With goose bumps, the hypothalamus is sending messages through the nerves to contract muscles in the skin, in effect creating 'goose bumps'. More chemical research needs to be done to further understand the pathways of emotional responses to music.

can you come up with some analogy, for example, to behavior of other animals? It seems that as is your answer is too non-specific, would be cool to add details


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What is the name of this plant (see pic)?
This plant was found growing in Colombia.

@j.musser is probably a good person to ask... also consider instead asking on the gardening stack exchange

@GriffinEvo Thank you, I guess I'd agree with Luigi on Lupinus :)

Better suited to [gardening.se]?

That looks like a member of genus Lupinus, flowering plants native to North and South America:

I couldn't find a good resource for species of Lupinus in Columbia so I can't help you with the species, unfortunately.

Thank you! It's size confused me a little: it's much bigger compared to the ones I've seen in Europe, and it has smaller flowers.


text:
Maximum heart rate increase/decrease while physically active
I'm a computer scientist and implementing a heart-rate monitor for physical exercise. While I try to improve my code, I stumbled over the question how fast the heart rate can change within a given time slot.
Let's say someone in rest has a pulse of 60bpm. If he now gets physically active and exploits himself, how much time will it take at least to reach his maximum pulse of (supposed) 200? How long will it take, after hitting that point to slow down his heart rate again?
Or to rephrase the question, so it fit's more to my specific code-problem.
How much can the pulse change (increase/decrease) within a small time-slot of, let's say 8 seconds.
Annotation:
Susan pointed out, that might be dependent on the trainings-state of each individual. If so, what is (might be) the highest possible increase/decrease?
I suppose the pulse increase of a bad trained person might be faster then that of a well trained person, whereas the the decrease of the pulse should be way faster of that trained person.
Annotation 2:
The question was already posted here: How quickly can the human heart rate rise and fall.
But not really answered. The answer here says, the rate can change within one beat. But it does not say by how much it can change, which bothers me. Especially how much it can change within a given period.

Are you looking for a constant? To my understanding, this parameter is highly dependent on age and training status.

I think there will be too many variables for this to get a precise answer - if I were you I'd ask myself whether it is important to track the heart rate so precisely, if it's for exercise then sampling/recording the heart rate every 5 seconds or 10 seconds isn't going to make much difference to the data.


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How to understand relatedness in an infinite island model?
My understanding is that the relatedness coefficient in kin selection models measures positive assortment. That is, altruism is more likely to evolve if altruists tend to interact with other altruists. As an example, consider a general model of kin selection due to Queller (Queller 1992 "A general model of kin selection"). According to this model, relatedness is viewed as the regression coefficient $\beta_{G'G}$, where $G$ is the individual's frequency of the altruism allele and $G'$ is the average value of $G$ of an individual's neighbors. Queller's definition of relatedness is a measure of positive assortment because, by definition,
$$\beta_{G'G} = \frac{E(G'\mid G) - E(G')}{G - E(G)}$$
If relatedness is positive, if the focal individual is more altruist than average ($G-E(G)>0$), then this individual will tend to interact with individuals that are more altruist than average ($E(G'\mid G) - E(G')>0$).
Now, in Taylor's 1992 kin selection model in patch-structured populations with asexual reproduction ("Altruism in viscous populations"), relatedness $R$ is defined as the recursion (p. 354):
$$ R' = \frac{1}{N} + \frac{N-1}{N}s^2R$$
where $N$ is the number of individuals in each patch and $s$ is the probability the offspring will remain on the natal patch (link to a question about $s$). Here $R$ doesn't seem to be a measure of positive assortment, but simply the chance that two alleles picked at random within the patch will be of the same type.
On one hand, $R$ in standard models of kin selection (e.g., Queller's) measures positive assortment. On the other hand, Taylor's kin selection model for patch-structured populations $R$ does not seem to measure positive assortment within a group but the chance that two random individuals share the same allele.
I tended to think of Queller's model as a general formulation of kin selection (accordingly, Taylor's model would be a particular instance of Queller's model). But, as I tried to explain above, my impression is that Taylor's model is an entirely different way of modeling kin selection ($R$ does not measure positive assortment). Am I missing some connection between Queller's and Taylor's models of kin selection? Or is there any way of connecting the two definitions of relatedness mentioned above?


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Why structures of many proteins are still unknown?

https://www.learner.org/courses/biology/textbook/proteo/proteo_3.html
Despite advances in techniques for determining protein structure, the structures of many proteins are still unknown...

My question is why? We have many researchers all over the world. We know what our 3 billion DNA is. The governments invest billions of money into medical research. We should have sufficient human resources and technology.
What's stopping us determining structure for all proteins? We need more money? We need more people? We need better technology?

How much you already know about determining protein structures?

Instead of standing on a soap box making speeches, how about making an effort to find the answer yourself? This is what is expected of people who post to SE Biology.

What's stopping us determining structure for all proteins? We need
  more money? We need more people? We need better technology?

Each unknown structure has a different story. Importantly, not all of the proteins have well-defined structures. But this hasn't historically been appreciated, so lots of effort has been spent structurally resolving regions that are not that ordered. But there are many other explanations for a protein's structure being unknown, most of which fit into two categories.
The putative function can be uninteresting. Most researchers only express and purify proteins that are potentially interesting. There is usually some genetic or pharmacological manipulation that implies a given protein is "interesting." Only the large scale structural genomics projects randomly solve structures, aiming for completion. Even then, these projects have to prioritize proteins that look feasible "on paper."
The protein can resist structure determination Lots of proteins are difficult to express and purify, for a multitude of reasons. For example, the table I just copied from the NorthEast Structural Genomic consortium summarizes their pipeline as follows:
Production stage
targets
cloned          25,924
Expression tested           24,479
Expressed (E > 0)           18,992
Expressed-Soluble (S > 0)           13,946
Expressed-Soluble (ES >= 9)             11,054
Fermented           9,440
Purification attempted          8,691
Purified (>= 0.5 mg yield)          6,493
HSQC spectra recorded           2,853
"Good" HSQC spectra             1,476
HTP crystal screening           2,845
Crystal hit             1,480   1,672
X-Ray structures            608
NMR structures          496
Total structures            1,061

So using a combination of X-ray crystallography and solution-state NMR spectroscopy, they solved 1061 structures of 25924 clones, almost all of which were tested for expression. (http://nesg.org/statistics.html)
Crystallography is as automated as it can get right now. Protein NMR and cryoEM have their own niches, and will continue to fill out the gaps. But the science is difficult, and of the well-defined structures, there are still many that haven't been well-described.
(If we could really solve the protein folding problem, we wouldn't need to solve structures at all...)

Also consider that very often the structure of an individual polypeptide (i.e. single coding gene's product) is meaningless because it only functions as part of a complex, in which its structure is different.

The gold standard for determining protein structures is x-ray crystallography. However, in order to perform x-ray crystallography you have to obtain your protein in crystalline form. Many proteins are not soluble in water, so crystallization is difficult or impossible. See this answer for information about the difficulty of crystallizing trans-membrane proteins.

The answer is most likely priorities. Yes, the process is easier, but it still requires at least some effort and time, which is better spent on other things. As such, grants are unlikely to be given for such trivial work - and this is researchers' job, so if they're not going to be paid for it, they probably won't do it.


text:
Why not replace the human genome with it's decoy genome version?

https://www.biostars.org/p/73100/
http://www.cureffi.org/2013/02/01/the-decoy-genome/

The decoy genome is an improved human genome from the regular reference. It's faster for mapping and it reduces false positive rate.
In this case, there shouldn't be any reason to use the regular human reference, because the decoy genome is simply better and faster. For instance, it has DNA sequence for common virus genomes presented in humans. It has everything in the regular reference.
Q: What's the point of the regular human reference (e.g. hg38) if the decoy version is more accurate and is a better representation for the human variation.

To clarify, when you say "decoy genome" you hopefully mean the decoy genome and the reference genome together; the decoy genome itself is not a complete reference, but a collection of contigs assembled from reads that didn't map to the current reference genome, but are known to be human sequence.
The decoy genome certainly seems to be a useful supplementary data set for speeding up read mapping to the human genome but it cannot be better than the reference because it is not comparable to the reference.

But you just said it has everything in the regular reference? And it has sequences not in the regular version too. Shouldn't it be just better?

The decoy genome does not have everything in the reference. It is separate from the reference; it only contains sequence that couldn't be mapped to the current version of the reference. From your second link: "an entire ‘decoy genome’ to capture reads from human exome or whole genome sequencing that fail to align to the human reference genome."

Quote: "If you’re just starting alignment now, you can use hs37d5.fa.gz, which is all of human genome b37 plus the decoy" in the second link. I think the decoy version does have everything in the regular reference.

No, hs37d5.fa.gz has the "human genome b37 plus the decoy". The b37 reference genome and the decoy genome are separate sequences, but they are combined in that gzipped FASTA file.


text:
Is there any way a human could whistle and be unable to speak?
Is there any situation anatomically, where a human could understand the speech of others perfectly, without any capabilities of speech themselves, but would retain the ability to whistle with a tune?
I was led to believe that the larynx is the most important thing for creating the sounds in speech, however I don't know if a damaged larynx would necessarily not allow someone to speak.
I would suggest someone who has no tongue cannot speak coherently, however I would imagine that the tongue is instrumental (pardon the pun) in creating a range of sounds whilst whistling.
Is there perhaps a part of the brain that could be affected in order to stop someone speaking? Or would this also mean the person could not understand speech either?

Thanks for splitting your questions - it is appreciated.

You can make a whistling sound by pushing air through your teeth.

Short answer(s)
Someone with a damaged larynx may still speak with the use of a speech aid (electronic larynx).
The ability to understand speech does not necessarily mean one can speak normally. There are neurological disorders where folks can understand speech, but have difficulty producing it.
Background
A full removal of the larynx (laryngectomy) prevents the patient to produce speech because the vocal chords are removed. However, with the use of a voice prosthesis this can be solved (Cancer Research UK).
There are neurological disorders where one can still understand speech, but has impaired ability to generate it, called apraxia. Apraxia can be acquired or congenital (developmental) (NIH). The developmental type is not well understood a the neural level (Dyspraxia Foundation UK). The acquired type is known to occur through damage to specific brain areas, for example due to trauma or stroke. One of these areas associated with acquired apraxia is Broca's area (Graff-Radford et al., 2014), which is a brain area intimately associated with the production of speech .

Broca's area. Source: UC Irvine
However, although damage to this area can result in pure apraxia (i.e., impaired speech but normal speech understanding), it can also result in Broca's aphasia, which means that language understanding is also impaired (American Heart Association). Moreover, regarding your specific question, apraxia is characterized by impaired speech, but not a total lack of speech production.
Reference
Graff-Radford et al. Brain & Language 2014;129:43–6

You can whisper without a larynx. "Patients who have undergone partial or full laryngectomy are typically unable to speak anything more than hoarse whispers, without the aid of prostheses or specialized speaking techniques." (source)
So the remainder of the vocal tract is capable of enough modulation to present typical human speech in many languages.
I expect, however, that the tongue, throat, jaw, and facial muscles figure very heavily into this modulation.
If these muscles become paralyzed or otherwise unusable, it's reasonable to assume that they may still be able to whistle with the use of the lungs and hands or fingers.
Hands free whistling, however, would require at least teeth and lips, or teeth and tongue, or lips and tongue - one surface to control the flow of air over the other surface that produces the whistle.  With control over just these few things, one could likely communicate to a reasonable degree in many languages.

Speech is generated by generating a frequency spectrum with the vocal folds, and then filtering it with the upper vocal tract.  Whistling is done by blowing air over shaped tongue and lips.
So, give someone a laryngectomy.  They still retain the upper vocal tract (and so can still whistle), but they cannot generate the source vibrations to speak.  Do it as an adult and they probably understand the speech of others.

It seems that speech can be recovered after total glossectomy, although it's not a short or an easy process.
Here's one case:

a case study, reporting the evaluation and evolution findings of the speech-language pathology rehabilitation of the swallowing and speech functions of a 58-year-old man submitted to total glossectomy in June 2009. After evaluation, the subject was diagnosed with severe mechanical oropharyngeal dysphagia and alteration in speech articulation. Speech rehabilitation used direct and indirect therapies. Indirect therapy focused on oral motor control, sensitivity, mobility, motricity, tonus and posture of the structures adjacent to the resected tongue. Direct therapy used the head back posture maneuver to help the ejection of food into the pharynx. The patient started exclusive oral feeding, except for solid foods, after ten months in treatment. Over-articulation, speed and rhythm exercises were used to improve speech intelligibility. Thus, the results of speech-language pathology intervention were considered positive, and the patient was discharged after a year in treatment.

Speech therapy in total glossectomy - case study, Vieira 2011


text:
What are respirasomes?
I have read wikipedia but don't understand it well. Do we mean Complex I, II,III and IV when we say respirasomes?

I have not heard of this term in so many years of studying biology. Whenever you are asking questions on terminology, always provide the context.

I could not add the context because it appeared as a question in one of our college exams.

@WYSIWYG Never heard of it either, sounds like it might be a 'kitchen table' terminology used by a handful (or just one!) researcher. Bit like 'quinary structure' which is meant to be assemblies of quaternary structures...

What the article is saying, is that there are several respirasomes, each of which consists of multiple other mitochondrial complexes or cytochromes. The article makes an example of three commonly observed respirasomes when it says:

The most common supercomplexes observed are Complex I/III, Complex
  I/III/IV, and Complex III/IV.

So, the short answer to your question is that a respirasome refers to a set of multiple cytochromes, whose combined action leads to respiration.
This paper goes into more detail, and reiterates that the term "respirasome" is used to describe multiple cytochrome supercomplexes.