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population. The fittest individuals are more likely to be selected for reproduction (retention or duplication), while recombination and mutation modify those individuals, yielding potentially superior ones.

EAs are one kind of evolutionary computation and differ from genetic algorithms. A GA generates each individual from some encoded form known as a "chromosome" and it is these which are combined or mutated to breed new individuals.

EAs are useful for optimisation when other techniques such as gradient descent or direct, analytical discovery are not possible. Combinatoric and real-valued function optimisation in which the optimisation surface or fitness landscape is "rugged", possessing many locally optimal solutions, are well suited for evolutionary algorithms.

Last updated: 1995-02-03

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} which incorporates aspects of naturaleionary computation} and differ from

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