Emmanoulidis, Christos and Hunter, Andrew (2000) A comparison of crossover operators in neural network feature selection with multiobjective evolutionary algorithms. In: Genetic and Evolutionary Computation Conference GECCO 2000, 8-12 July 2000, Las Vegas, USA.

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Abstract

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

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Genetic algorithms are often employed for neural network feature selection. The efficiency of the search for a good subset of features, depends on the capability of the recombination operator to construct building blocks which perform well, based on existing genetic material. In this paper, a commonality-based crossover operator is employed, in a multiobjective evolutionary setting. The operator has two main characteristics: first, it exploits the concept that common schemata are more likely to form useful building blocks; second, the offspring produced are similar to their parents in terms of the subset size they encode. The performance of the novel operator is compared against that of uniform, 1 and 2-point crossover, in feature selection with probabilistic neural networks .
Keywords:	Neural networks, Genetic algorithms
Subjects:	G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Tammie Farley
Date Deposited:	30 Jun 2009 14:18
Last Modified:	13 Mar 2013 08:32
URI:	http://eprints.lincoln.ac.uk/id/eprint/1878

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