(EA) An algorithm which incorporates aspects of natural selection or survival of
the fittest. An evolutionary algorithm maintains a population of structures
(usually randomly generated initially), that evolves according to rules of
selection, recombination, mutation and survival, referred to as genetic
operators. A shared "environment" determines the fitness or performance of each
individual in the 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
EVGA « evil « evil and rude « evolutionary
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