2013年
Self organizing classifiers and niched fitness
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
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- 開始ページ
- 1109
- 終了ページ
- 1116
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1145/2463372.2463501
- 出版者・発行元
- ACM
Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organizing Classifiers is proposed which faces this problem from a different perspective. Instead of balancing the pressures, both pressures are separated and no balance is necessary. In fact, the proposed algorithm possesses a dynamical population structure that self-organizes itself to better project the input space into a map. The niched fitness concept is defined along with its dynamical population structure, both are indispensable for the understanding of the proposed method. Promising results are shown on two continuous multi-step problems. One of which is yet more challenging than previous problems of this class in the literature. Copyright © 2013 ACM.
- リンク情報
- ID情報
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- DOI : 10.1145/2463372.2463501
- DBLP ID : conf/gecco/VargasTM13
- SCOPUS ID : 84883056658