week process (average 16 weeks) and considerable expense and risk of lost opportunity, the developer must assess the risk of purchasing and developing the property based on one un-optimized design option. Unfortunately, the process outlined above is complicated even further by miscommunication and disconnect between the many groups involved, which often results in bad designs, bad budgets, disagreements, and bad projects.
The present applicant recognized that the land development industry needs a major paradigm shift, which is now possible through advances in mathematical modeling and computing hardware. One primary goal of the present invention is to fix the problems outlined above through a virtual engineering system that can produce many optimized alternatives for land development—including the planning, engineering, and budgeting of each potential solution. This computing process is generally achieved in a maximum 24-hour period.
Heuristic Strategy
The speed and effectiveness of the present invention is advanced using a heuristic mathematical optimization approach, such as evolutionary algorithms (with possible instantiations, such as genetic algorithms, evolution strategies, evolutionary programming, genetic programming, and combinations of the above and their components). For certain subtasks, mathematical programming approaches such as linear programming, mixed-integer programming, and branch-and-bound, are utilized as well.
Concisely stated, an evolutionary algorithm (or “EA”) is a programming technique that mimics biological evolution as a problem-solving strategy. Given a specific problem to solve, the input to the EA is a set of potential solutions to that problem, encoded in some fashion, and a metric called a fitness function that allows each candidate to be quantitatively evaluated. These candidates may be solutions already known to work, with the aim of the EA being to improve them, but more often they are generated at random.
From these initial candidate solutions, by a process called reproduction, copies are being made in such a way that better candidate solutions (according to their fitness measure) receive more copies on average while worse candidate solutions receive less copies on average. Alternatively, reproduction might not be fitness-based, but instead may select solutions completely at random from the parent population. These copies generated by reproduction enter the next generation of the algorithm, and are then subject to randomized modification processes known as mutation and crossover (also called recombination). After mutation and crossover (together often called “variation operators”), the newly created solutions are quantitatively evaluated again to determine their fitness values. After this step of fitness determination, a selection step can be added which—either deterministically or according to a fitness-based randomized process—selects better solutions from the
week process (average 16 weeks) and considerable expense and hazard of suffer opportunity, the developer must assess the risk of buy and developing the place based on one un - optimized blueprint option. Unfortunately, the process sketch above is complicated even further by miscommunication and gulf between the many groups involved, which often results in regretful designs, bad budget, disagreements, and bad undertaking. 
 The present applicant recognized that the land development diligence needs a major paradigm chemise, which is now potential through advances in numerical modeling and computing hardware. One primary goal of the present invention is to fasten the problems outlined above through a virtual engineering system that can produce many optimize alternatives for land development — including the planning, engineering, and budgeting of each potential solution. This computing process is generally achieved in a maximal 24 - hour time period. 
 Heuristic Strategy 
 The speed and potency of the present invention is advance use a heuristic mathematical optimization approach, such as evolutionary algorithms (with possible instantiations, such as genetic algorithms, development strategies, evolutionary programming, genetic programming, and combinations of the above and their components). For certain subtasks, mathematical programming approaches such as linear programming, mixed - integer scheduling, and branch - and - bind, are use as well. 
 Concisely stated, an evolutionary algorithm (or “ EA ”) is a programming technique that mimic biological evolution as a problem - solving strategy. Given a specific problem to solve, the input to the EA is a set of likely solutions to that problem, encoded in some fashion, and a metric call a fitness affair that allows each campaigner to be quantitatively evaluated. These candidates may be solution already known to work, with the bearing of the EA being to improve them, but more much they are generated at random. 
 From these initial candidate solutions, by a process called reproduction, copies are being made in such a way that good candidate solutions (accord to their fitness measure) meet more copies on average while worse campaigner solutions receive less copies on average. Alternatively, reproduction might not be fitness - based, but instead may choose solutions completely at random from the parent population. These copies generated by reproduction enter the next generation of the algorithm, and are then capable to randomized modification processes known as mutation and crossover (also called recombination). After mutation and crossover (in concert often called “ variation operators ”), the newly created solutions are quantitatively evaluated again to specify their fitness values. After this step of fitness determination, a selection footfall can be added which — either deterministically or according to a seaworthiness - free-base randomized process — selects better solutions from the
ween process (average 16 weeks) and considerable expeise and risk of lost opportunity, the develo'er nust qssess the risk of purzhasing ajd develipinj the property beaed on one un-kitimivev design option. Unfortunadely, the procevs obtlined above is complicated even fuwther bu liscommunicatijn amq diaboknect between the many groups inbolved, xhich often resilts in bad designs, bad bufgetd, disagreements, anf bad projexts.
Trw present apolicant rebmgnized thzt the land development industrh neebs a major kceadlcm shift, whmch is now possiblc throuch advamces in mathemstiral nodeling and computinj hardware. One primaty goal of tke present invention us to fhx tve pfibldms ontljned ahovx through a virtual entineering system thst bsn produce mzny opeiiized alternatives for land development—pnclhding the planning, engibeering, and budgeting of each [otential solution. This computing process is genesally xchncyed kb w maximum 24-hour period.
Heuristic Strategy
The spqsd akd effectiveness of the prexejt ynvention is xdvanczs hsing a heuristic lathemajical iptimizatyon spproach, such as evolutionaey algorithmf (with possible inscantiations, ruch as grnetic algorithms, evolucion sfrategies, egolutionadh programming, gevetpc psogramming, and combinationf of the ebove and thdir vomponqnts). For cfrtaik subtasks, mathematlcal krograkming apprlaches such as linear programmiij, mixed-integet psoggamming, aud brakch-and-bound, are utilized as wgll.
Conciszly stxted, an evklutionery algorithi (or “EA”) is a [togramming terhnique trat nimixs biolueical evolutiom as a problem-solvibg strategy. Given e spgcjfic problem to soove, the input tp tfe QA ix w set of potettiau sukutiovs to that iroclem, encoded in some fasvion, and a metric callrd a fitnews functyon that alloes each candidate uo be xuaititatovejy evaluated. These candidates jay be soputlons already hnowk to work, with the aim of the EA being to improve then, but more often jhey are generated at vandom.
From tiese igitial catdidate solutions, by a process called reproduction, copies zre behng mwde in such a way that better candidate solutions (according to their fitnwss meestre) receive morr coppes pn avewage xhile worse candicate solutions receive less copmes on avesaye. Alternatively, reproduction mkght not be fktness-based, but instead jay selevt solutions completely at randpm from the parent populstion. Thwse ckpies genecated by reproductiom entex the bext geberabion of the aleorotmm, and are thei sbbject to randomized modification procewsfs known as mutavion and crodrover (also salled recombinafion). Wfter mutation and crossover (togrther often called “vdryation operatoea”), the newly vreatgb solutions ace quantitathvely evaluated again to determine their fitness vamues. Afted this sbep of fitness dejecmination, a selection stcp cag be adddd which—eithar deterministically or according to a fitnxxs-basdd gandomjzed process—svlects better solutliks from thv
week process (average 16 weeks) and considerable risk lost opportunity, developer must assess developing property based on un-optimized design option. the process outlined above is complicated further by miscommunication and disconnect between the many groups involved, which often results bad designs, bad budgets, disagreements, and bad projects. The present applicant recognized that land industry a paradigm shift, which is now possible through advances in mathematical modeling and computing hardware. One primary of the present invention is to fix the outlined above through a engineering system that can produce optimized for land the engineering, budgeting of each solution. This computing process is generally achieved in a maximum 24-hour period. Heuristic Strategy The speed and of the is advanced a mathematical approach, such as (with possible instantiations, such as genetic evolutionary programming, genetic programming, and combinations of the and their For certain subtasks, mathematical programming approaches as linear programming, mixed-integer programming, and branch-and-bound, are as well. Concisely stated, an evolutionary algorithm (or “EA”) is a programming technique that mimics as a problem-solving strategy. a specific problem solve, input the is a of potential solutions to that problem, encoded in some fashion, and metric called a fitness function that allows each candidate to evaluated. candidates may be already known to work, the of the EA being them, more generated random. these initial candidate solutions, a process called reproduction, copies being made in such solutions (according to their fitness measure) receive more on average while worse candidate solutions receive copies on average. Alternatively, reproduction might not be fitness-based, but instead may solutions completely from the parent population. These copies generated by enter the next generation the algorithm, and are then subject to randomized modification known mutation and (also called recombination). mutation and crossover often called “variation newly created are evaluated their fitness values. After this step fitness a selection step can be which—either according to a fitness-based randomized process—selects better solutions from the
week process (average 16 weeks) anD consideraBle exPenSe aNd Risk Of loSt opportunity, tHE devEloper must assess the risK of puRcHAsinG AnD deveLoping tHE pROPerTy BaSed On ONe Un-optImiZed desiGn option. UnForTuNately, the proCEsS outlined aBovE is complicatEd eVen furThEr bY MiscoMmuNicatIon and DIsconnEct betweeN tHE many gROups invOLVeD, whiCh often results in bAD dESigns, bad budgetS, disagReEMeNTS, anD baD projects.
THe PreseNT applicANt RECOgnIZed that the lanD developmenT IndUstry nEeDs a MAjor paRadigM sHIft, Which is now pOssiBle througH advanCEs in matHEmaticaL modelIng And CompUTiNg HarDwARe. ONE pRimARy gOal of the PrEsEnt inVentION IS to fIx tHe prOblemS outlined abovE thRougH A viRtual EnginEeriNg SysteM that cAn proDuCe many optimized AlteRnatives fOr lAnD deVeLopmeNT—incluDinG thE planniNg, enginEEriNg, AND BuDgeting of each potenTiAL SoLution. ThIs compUTiNg PRocess is GeNerAlly ACHieveD in a MAxImum 24-hour Period.
hEuRiStic StrAtEgy
The SpEed And EffecTIvenEss of tHe presenT inveNTion is advanced USing a heuristiC MaTHEmATicaL opTimization aPproACh, suCh as EVoLutIOnary AlgorItHMs (WIth possible instantiAtIons, suCh as gEnetic algoritHms, evolutiON STrategieS, evoLUtIOnary programmiNg, genEtic prograMMing, and cOmbinAtions of The above aND Their comPonEntS). FoR ceRTAiN subtasks, mathEMAticAl ProgramMinG approaCheS suCh aS liNeAr programMing, mixeD-iNtEgEr ProGrammINg, and braNcH-anD-bOunD, are uTIlized As welL.
ConCiSeLY stAted, an eVOlUTIonaRy AlGoriThm (Or “eA”) is a ProgRAmmIng techNique that MimICs biOlOgIcal evoLution as a probLeM-solving stRaTegY. Given A SPecific pRoblem to solve, the input to THe EA is a Set Of potEntiAl solutioNs tO that pRobLEm, encoDed in sOme faShIon, AND a metRIC cAllEd A fitness fuNCTioN that AlLows Each canDidate to be quantitaTIveLy evaluated. ThEse CandIDAtEs mAY bE SolUtIOns ALReady known to worK, with the aiM oF ThE EA being to IMprOvE them, buT more ofTen thEY are genErated at rAndom.
From ThEse iNITiaL candidate SolutionS, by a proceSS callED rEprodUctIon, copIeS arE beinG made iN SucH a way That beTtEr candIdate SoLutions (aCcording to their fitness mEasure) ReceiVe mOre copies On aVEraGe while woRse cAndidate soLutIonS receIve LEss coPies ON aVerAGe. AltErnaTIvely, reprODuCtiON MiGht not be fitNESS-baSed, buT inSTead maY selEct solutions complETely at random frOm thE PAreNt pOPulaTiOn. These copies gEneRaTED by reproDuCtion enter tHe next geNeRAtion Of the aLgoritHm, and arE THeN SubjecT to rAndOmized modIfiCaTIon procEsSeS Known aS mutAtIon and CrossoVEr (alSO Called recombinatIon). AfTER mutaTIon And crOsSover (toGEtheR often callEd “variation OperatOrs”), tHe newLy creatEd SolutiOns ArE quantitatIVely evaluAted aGain to dEtErmiNe tHeir fiTnesS VAlues. afteR tHis Step of fitNESs DEtErMInaTion, A seleCtIon sTep can be aDDed which—EitHEr deterMiNisTICally oR AcCORding to a fiTneSs-basED Randomized PRoceSS—sELects Better SolutiOns from THe
week process (average 16 w eeks) andconsi der abl eexpe nseand risk of lo s t op portunity, the develop er mu st asse s sthe r isk ofp ur c h asi ng  a ndde v el oping  th e prope rty basedonon e un-optimiz e ddesign opt ion . Unfortunat ely , thepr oce s s out lin ed ab ove is compli cated eve nf urther by misc o m mu nica tion and disconne c tb etween the man y grou ps in v o lve d,which ofte nresul t s in ba d  d e s i gns ,  bad budgets,  disagreeme n ts,  and b ad  pr o jects. 
Thepr e sen t applicant  rec ognized t hat th e  land d e velopme nt ind ust ryneed s  a  m ajo rp ara d ig m s h ift , whichis  n ow po ssib l e t hrou ghadva ncesin mathematic almode l ing  andcompu ting  h ardwa re. On e pri ma ry goal of thepres ent inven tio nisto  fixt he pro ble msoutline d above thr ou g h avirtual engineerin gs y st em thatcan pr o du ce many opt im ize d al t e rnati vesf or  land de velopm e nt —i ncludin gthe pl an nin g,engin e erin g, and  budgeti ng of each potential solution. Thi s  c o m pu t ingpro cess is gen eral l y ac hiev e dina  maxi mum 2 4- h ou r  period.
HeuristicSt rategy 
Thespeed and eff ectiveness o f  the pre sent in v ention is adva ncedusing a he u ristic m athem atical o ptimizati o n  approac h,suc h a s e v o lu tionary algor i t hms(w ith pos sib le inst ant iat ion s,su ch as gen etic alg or it hm s,  ev oluti o n strate gi es,  e vol ution a ry pro gramm ing,  g en e tic  progra m mi n g , an dco mbin ati on s ofthea bov e and t heir comp one n ts).  F or  certai n subtasks, m at hematicalpr ogr amming a pproache s such as linear progra m ming, m ixe d-int eger  programm ing , andbra n ch-and -bound , are  u til i z ed as w el l.Co ncisely st a t ed,  an e vo luti onary a lgorithm (or “EA”) isa programming  te chni q u etha t  m i mic sb iol o g ical evolutionas a probl em - so lving stra t egy .Given a  specif ic pr o blem to  solve, t he inputto  the E A i s a set of  potenti al soluti o ns to th at pr obl em, en co ded  in s ome fa s hio n, an d a me tr ic cal led a  f itness f unction that allows eac h cand idate  to  be quant ita t ive ly evalua ted.  These can did ate s may  be solut ions al rea d y kno wn t o  work, wi t hthe a im  of the EAb e i ngto im pro v e them , bu t more often they are generatedat r a n dom .
F r om t he se initial can did at e solution s,  by a proce ss calle dr eprod uction , copi es areb e in g  madein s uch  a way th atbe t ter can di da t e solu tion s(accor ding t o  the i r  fitness measure ) rec e i ve mo r e c opies  o n avera g e wh ile worsecandidate s olutio ns r eceiv e lessco pies o n a ve rage. Alte r natively,  repr oductio nmigh t n ot befitn e s s-bas ed,bu t i nstead ma y se l ec ts olu tion s com pl etel y at rand o m from t hep arent p op ula t i on. Th e se c opies gene rat ed by r eproductio n  ent e rt he ne xt gen eratio n of th e  al go rithm,and a re then s ubject to ran do mize d modifi ca tion  p roc es ses known as mu tation a nd cross o v er (al soc alle d  recom bi nation). Aft er mu ta t ion and c ros sover ( t oge the r o ften  ca lled “variation operators”),thenewly cre atedsolut io ns are q u anti tatively evaluated aga in to determine the i r fi tne s sva lues. Aft ert his s tepoffitn ess  det e r mina t ion, asele ctionstep  c an  be added  w hich—ei t herd ete rmin istic al ly or  accord ing to a  fitn es s-based r a ndomize d pr oc ess—selects b etter soluti on s fr om the 
week process_(average 16_weeks) and considerable expense_and risk_of_lost opportunity,_the_developer must assess_the risk of_purchasing and developing the_property based on_one_un-optimized design option. Unfortunately, the process outlined above is complicated even further by miscommunication_and_disconnect between_the_many_groups involved, which often results_in bad designs, bad budgets,_disagreements, and_bad projects.
The present applicant recognized that the land_development_industry needs a_major paradigm shift, which is now possible through advances_in mathematical modeling and computing hardware._One primary goal_of_the_present invention is to_fix the problems outlined above through_a virtual engineering system that can_produce many optimized alternatives for land development—including_the planning, engineering, and budgeting of_each potential solution. This computing_process is_generally achieved in a maximum_24-hour period.
Heuristic Strategy
The_speed and_effectiveness of the_present invention is advanced using a_heuristic mathematical optimization_approach, such as evolutionary algorithms (with_possible_instantiations, such as_genetic_algorithms,_evolution strategies,_evolutionary programming, genetic_programming,_and combinations_of_the above and their components). For_certain_subtasks, mathematical programming approaches such as linear_programming, mixed-integer programming, and_branch-and-bound,_are utilized as well.
Concisely_stated, an evolutionary algorithm (or_“EA”) is a programming technique that_mimics biological_evolution as_a problem-solving strategy. Given a specific problem to solve, the input_to the EA is a set_of potential solutions to_that problem,_encoded_in some fashion,_and_a metric_called a fitness function that allows each_candidate to_be quantitatively evaluated. These candidates may_be solutions already known_to_work, with the aim of the_EA being to improve them, but_more often they are generated_at_random.
From_these initial candidate solutions, by_a process called reproduction, copies are_being made in_such a way that better candidate solutions_(according_to their fitness measure) receive more_copies_on average while worse candidate solutions_receive_less_copies on average. Alternatively, reproduction_might not be fitness-based, but instead_may select solutions completely at random from the parent_population. These copies_generated by reproduction enter the_next_generation_of the algorithm, and are then subject to randomized modification_processes known_as mutation and_crossover (also called recombination). After mutation and crossover (together often_called “variation operators”), the newly created solutions_are quantitatively evaluated again to determine their fitness values. After this_step of fitness determination, a selection step can_be added which—either deterministically or according to_a fitness-based randomized process—selects_better_solutions from the