Genetic Algorithm

A genetic algorithm (GA) is a search  used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).
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(GA) An evolutionary algorithm which generates each individual from some encoded form known as a "chromosome" or "genome". Chromosomes are combined or mutated to breed new individuals. "Crossover", the kind of recombination of chromosomes found in sexual reproduction in nature, is often also used in GAs. Here, an offspring's chromosome is created by joining segments choosen alternately from each of two parents' chromosomes which are of fixed length.
GAs are useful for multidimensional optimisation problems in which the chromosome can encode the values for the different variables being optimised.
Illinois Genetic Algorithms Laboratory (IlliGAL).
(1995-02-03)

genetic algorithm

A search algorithm which locates optimal binary strings by processing an initially random population of strings using artificial mutation, crossover and selection operators, in an analogy with the process of natural selection (Goldberg, 1989).
See also, Neural Networks .

genetic algorithm
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