CODEWITHSUNDEEP
evolutionary-algorithmmutationdifferential-evolution
JAV
JAV

Reputation: 308

Why differential evolution works so well?

What is the idea behind the mutation in differential evolution and why should this kind of mutation perform well? I do not see any good geometric reason behind it.

Could anyone point me to some technical explanation of this?

Upvotes: 2

Views: 794

Answers (1)

Matthew Woodruff
Matthew Woodruff

Reputation: 453

Like all evolutionary algorithms, DE uses a heuristic, so my explanation is going to be a bit hand-wavy. What DE is trying to do, like all evolutionary algorithms, is to do a random search that's not too random. DE's mutation operator first computes the vector between two random members of the population, then adds that vector to a third random member of the population. This works well because it uses the current population as a way of figuring out how large of a step to take, and in what direction. If the population is widely dispersed, then it's reasonable to take big steps; if it's tightly concentrated, then it's reasonable to take small steps.

There are many reasons DE works better than Goldberg's GA, but focusing on the variation operators I'd say that the biggest difference is that DE uses real-coded variables and GA uses binary encoding. When optimizing on a continuous space, binary encoding is not a good choice. This has been known since the early 1990s, and one of the first things to come out of the encounter between the primarily German Evolution Strategy community and the primarily American Genetic Algorithm community was Deb's Simulated Binary Crossover. This operator acts like the GA's crossover operator, but on real-coded variables.

Upvotes: 3

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