Abstract
Evolutionary algorithms (EA) are optimization algorithms inspired by the neo-dar winian theory of evolution. Those algorithms use a population of potential solutions which is gradually guided towards better solutions discovered by random variation. The neutral theory of evolution considers that the majority of mutations are selectively neutral or lethal. In this article we present the main results about neutral theory in the context of EA, and particulary how this theory affects studies of fitness landscapes. Fitness landscapes represent the set of potential solutions of a problem as an space equipped with a neighborhood relation and where the solutions have an height corresponding to their performance. This article will show academic and real problems related to neutral landscapes and the nature of EA dynamics on those landscapes. In order to improve the performances of EA, we then present technics based on the choice of redundant coding, just as the design of EA exploiting neutrality.
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