research-article

New entropy model for extraction of structural information from XCS population

Authors:

Jae C. OhAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 1283 - 1290

https://doi.org/10.1145/1569901.1570073

Published: 08 July 2009 Publication History

Abstract

We show that when XCS is applied to complex real-valued problems, the XCS populations contain structural information. This information exists in the underlying classifier space as the degree of uncertainty associated to the problem space. Therefore, we can use structural information to improve the overall system performance. We take an information theoretic approach, introducing a new entropy model for XCS to extract the structural information from dynamically forming substructures. Using this entropy model, we can collectively emphasize or de-emphasize the effect of an individual input. For a complex problem domain, we chose the speaker identification (SID) problem. The SID problem challenges XCS with a complex problem space that may force the learning classifier system to evolve large and highly overlapping population. The entropy model improved the system performance up to 5-10% in large-set SID problems. Furthermore, the entropy model has the ability to assist the population initialization in the beginning of the learning process by assuring a certain level of overall diversity.

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Cited By

Kuber KMohan CPelikan MBranke J(2010)Information theoretic fitness measures for learning classifier systemsProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830821(1885-1892)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830821

Index Terms

New entropy model for extraction of structural information from XCS population
1. Theory of computation
  1. Logic
  2. Models of computation

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 ACM.

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Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Cited By

Kuber KMohan CPelikan MBranke J(2010)Information theoretic fitness measures for learning classifier systemsProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830821(1885-1892)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830821

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