Non-Standard Crossover for a Standard Representation — Commonality-Based Feature Subset Selection
Conference Paper, Proceedings of 1st Annual Genetic and Evolutionary Computation Conference (GECCO '99), pp. 129 - 134, July, 1999
Abstract
The Commonality-Based Crossover Framework has been presented as a general model for designing problem specific operators. Following this model, the Common Features/Random Sample Climbing operator has been developed for feature subset selection--a binary string optimization problem. Although this problem should be an ideal application for genetic algorithms with standard crossover operators, experiments show that the new operator can find better feature subsets for classifier training.
BibTeX
@conference{Chen-1999-16641,author = {Stephen Chen and Cesar Guerra-Salcedo and Stephen Smith},
title = {Non-Standard Crossover for a Standard Representation -- Commonality-Based Feature Subset Selection},
booktitle = {Proceedings of 1st Annual Genetic and Evolutionary Computation Conference (GECCO '99)},
year = {1999},
month = {July},
pages = {129 - 134},
publisher = {Morgan Kaufmann},
keywords = {genetic algorithms, feature subset selection, machine learning, commonality hypothesis},
}
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