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In genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. It is analogous to reproduction and biological crossover, upon which genetic algorithms are based. Cross over is a process of taking more than one parent solutions and producing a child solution from them. There are methods for selection of the chromosomes. Those are also given below. ==Methods of selection of chromosomes for crossover== *Fitness proportionate selection (SCX) It is also known as fitness proportionate selection. The individual is selected on the basis of fitness. The probability of an individual to be selected increases with the fitness of the individual greater or less than its competitor's fitness. *Boltzmann selection *Tournament selection *Rank selection *Steady state selection *Truncation selection *Local selection 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Crossover (genetic algorithm)」の詳細全文を読む スポンサード リンク
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