Remembering the Future

One ramification of this is the “No Free Lunch” (NFL) theorems, which state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance.

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In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. However in the typical coevolutionary scenarios encountered in biology, where there is no champion, the NFL theorems still hold.

Topics: Evolution | AI | ArtificalLife

Labels: AI, ArtificalLife, evolution