Alternatives to evolutionary computing for structure, design and policy optimization (optimal structure search)? I once had this question https://math.stackexchange.com/questions/1083338/structural-design-meta-optimization-is-there-mathematical-theory-optimiza about the methods for finding optimal structures, designs and policies (as another kind of structures - set of rules), i.e. optimization of the structure of the models not just the parameters for some pre-selected structure. So far I am sure that the genes of evolutionary computation (EC) can effectively encode structure and evolutionary computing can perform effective search for more optimal structures. EC algorithms are quite sophisticated - there is evolutionary programming (for search of strings in some grammar) and there is cultural algorithms (which incorporates already known certain knowledge for speedup of the search). But are there some other kind of search procedures (besides evolutionary computing) for searching more efficient structures. EC is stochastic search methods and is there deterministic search methods for the search of optimal structure? I expect only key-words, pointers and trends.

Or - more clearly - is EC the most general search framework? Are there search alogrithms or approaches than can not be reduced to the string of application of crossover, mutation and other operators of EC?
At a broad level, there are two types of optimization: continuous optimization, and discrete optimization.  They tend to use different techniques.

Continuous optimization deals with problems where the parameters are continuous; methods include gradient descent, Newton's method, and other iterative methods, linear programming, and more.

Discrete optimization, or combinatorial optimization, deals with problems where the parameters come from a finite site; methods include integer linear programming, SAT, simulated annealing, particle swarm optimization, and more.

Genetic algorithms are another technique.  However, my experience is that they are rarely the best one; other methods often beat genetic programming and evolutionary optimization methods.  Your experience may vary.