Genetic algorithms are often employed for\ud neural network feature selection. The efficiency\ud of the search for a good subset of features,\ud depends on the capability of the recombination\ud operator to construct building blocks which\ud perform well, based on existing genetic material.\ud In this paper, a commonality-based crossover\ud operator is employed, in a multiobjective\ud evolutionary setting. The operator has two main\ud characteristics: first, it exploits the concept that\ud common schemata are more likely to form useful\ud building blocks; second, the offspring produced\ud are similar to their parents in terms of the subset\ud size they encode. The performance of the novel\ud operator is compared against that of uniform, 1\ud and 2-point crossover, in feature selection with\ud probabilistic neural networks
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