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I wonder why evolutionary algorithms never managed to be that successful.

• uoaei 9y ago
  They're quite successful on complex multiobjective problems, but you need a lot of compute power to cover the solution space sufficiently. Most problems either 1) don't have such stringent requirements on multiobjective optimization (i.e. the problem can be recast with a single objective function, likely one with a gradient), or 2) more typical machine learning algorithms are "good enough" and require less compute power. But for instance designing complex geometries under numerous constraints is well-suited to evolutionary algorithms. EAs also shine when the evaluation for fitness is computationally-intensive, as you can defer the fitness evaluations until absolutely necessary.
  • yters 9y ago
    My understanding is MOEAs are not a whole lot better than random search.