Adapting Crossover in a Genetic Algorithm
نویسنده
چکیده
Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Despite theoretical analysis, however, it appears difficult to predict when a particular crossover form will be optimal for a given problem. This paper describes an adaptive genetic algorithm that decides, as it runs, which form is optimal.
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