evolutionary algorithm

(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 evolutionary algorithms.

(1995-02-03)