Abstract
To improve the accuracy of clustering classification the Adaptive Genetic Algorithm was proposed. The code is float, the selection operator is rank-based fitness assignment and elitist model, the crossover operator is real valued recombination, the mutation operator is real mutation. The adaptive crossover probability and adaptive mutation probability are proposed, which consider the influence of every generation to algorithm and the effect of different individual fitness in every generation. Theory and experiment shows that the algorithm can get global optimum clustering center, and greatly improve the amplitude of operation.
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