Adaptive crossover using automata
Genetic Algorithms (GAs) have traditionally required the specification of a number of parameters that control the evolutionary process. In the classical model, the mutation and crossover operator probabilities are specified before the start of a GA run and remain unchanged; a so-called static model. This paper extends the conventional representation by using automata in order to allow the adaptation of the crossover operator probability as the run progresses in order to facilitate schema identification and reduce schema disruption. Favourable results have been achieved for a wide range of function minimization problems and these are described.
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