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

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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A comparison of crossover operators in neural network feature selection with multobjective evolutionary algorithms
Conference paper "A comparison of crossover operators in neural network feature selection with multobjective evolutionary algorithms"
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Official URL: http://www.cs.colostate.edu/~genitor/GECCO-2000/ge...

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 Science > School of Computer Science
ID Code:1878
Deposited By: Tammie Farley
Deposited On:30 Jun 2009 14:18
Last Modified:13 Mar 2013 08:32

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