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A comparison of crossover operators in neural network feature selection with multiobjective evolutionary algorithms

By Christos Emmanoulidis and Andrew Hunter

Abstract

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

Topics: G760 Machine Learning
Year: 2000
OAI identifier: oai:eprints.lincoln.ac.uk:1878

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Citations

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