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 .