Genetic Algorithm Operators

Genetic algorithms (GAs) are developed to mimic some of the processes observed in natural evolution. The underlying principles of GAs were first published by Holland [1962]. The mathematical framework was developed in the 1960s and is presented in his pioneering book [Holland, 1975]. Genetic algorithms have been employed primarily in two major areas:

In optimization applications, they have been used in many diverse fields such as function optimization, image processing, the traveling salesperson problem, system identification, and control.
In machine learning, GAs have been used to learn syntactically simple string IF-THEN rules in an arbitrary environment. Excellent references on GAs and their implementation and application are [Goldberg, 1989; Davis, 1991; Michalewicz, 1992].

The basic elements of a genetic algorithm involves three types of operators:

Selection, this operator selects chromosomes in the population for reproduction. The fitter the chromosome, the more times it is likely to be selected to reproduce.
Crossover, this operator randomly chooses a locus and exchanges the subsequences before and after that locus between two chromosomes to create two offspring. For example, the strings 10000100 and 11111111 could be crossed over after the third locus in each to produce the two offspring 10011111 and 11100100. The crossover operator roughly mimics biological recombination between two single−chromosome (haploid) organisms.
Mutation, this operator randomly flips some of the bits in a chromosome. For example, the string 00000100 might be mutated in its second position to yield 01000100. Mutation can occur at each bit position in a string with some probability, usually very small (e.g., 0.001).

Genetic Algorithm Operators

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