Non-Standard Crossover for a Standard Representation -- Commonality-Based Feature Subset Selection

Stephen Chen, Cesar Guerra-Salcedo, and Stephen Smith
GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, 1999.


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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.

Keywords
genetic algorithms, feature subset selection, machine learning, commonality hypothesis

Notes
Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems
Associated Lab(s) / Group(s): Intelligent Coordination and Logistics Laboratory

Text Reference
Stephen Chen, Cesar Guerra-Salcedo, and Stephen Smith, "Non-Standard Crossover for a Standard Representation -- Commonality-Based Feature Subset Selection," GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, 1999.

BibTeX Reference
@inproceedings{Chen_1999_557,
   author = "Stephen Chen and Cesar Guerra-Salcedo and Stephen Smith",
   title = "Non-Standard Crossover for a Standard Representation -- Commonality-Based Feature Subset Selection",
   booktitle = "GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference",
   publisher = "Morgan Kaufmann",
   year = "1999",
}