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Convergence (evolutionary computing) : ウィキペディア英語版
Convergence (evolutionary computing)

Precisely every individual in the population is identical. While full convergence might be seen in genetic algorithms using only crossover, such ''convergence'' is seldom seen in genetic programming using Koza's subtree swapping crossover. However, populations often stabilise after a time, in the sense that the best programs all have a common ancestor and their behaviour is very similar (or identical) both to each other and to that of high fitness programs from the previous (and future?) generations. Often the term ''convergence'' is loosely used.
==References==

(Foundations of Genetic Programming )

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