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Genetic operator
A genetic operator is an operator used in genetic algorithms to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover and selection), which must work in conjunction with one another in order for the algorithm to be successful. Genetic operators are used to create and maintain genetic diversity (mutation operator), combine existing solutions (also known as chromosomes) into new solutions (crossover) and select between solutions (selection).〔(【引用サイトリンク】url=http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/hmw/article1.html )〕 In his book discussing the use of genetic programming for the optimization of complex problems, computer scientist John Koza has also identified an 'inversion' or 'permutation' operator; however, the effectiveness of this operator has never been conclusively demonstrated and this operator is rarely discussed.〔(【引用サイトリンク】url=ftp://ftp.cis.upenn.edu/pub/hollick/public_html/genetic/node7.html#SECTION00023000000000000000 )〕
Mutation (or mutation-like) operators are said to be ''unary'' operators, as they only operate on one chromosome at a time. In contrast, crossover operators are said to be ''binary'' operators, as they operate on two chromosomes at a time, combining two existing chromosomes into one new chromosome.〔(【引用サイトリンク】url=http://kal-el.ugr.es/GAGS/gags-tutorial/node3.html )〕
==Operators==
Genetic variation is a necessity for the process of evolution. Genetic operators used in genetic algorithms are analogous to those in the natural world: survival of the fittest, or selection; reproduction (crossover, also called recombination); and mutation.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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