Self organizing classifiers and niched fitness

研究成果: 著書/レポートタイプへの貢献会議での発言

11 引用 (Scopus)

抜粋

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.

元の言語英語
ホスト出版物のタイトルGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
ページ1109-1116
ページ数8
DOI
出版物ステータス出版済み - 9 2 2013
イベント2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013 - Amsterdam, オランダ
継続期間: 7 6 20137 10 2013

出版物シリーズ

名前GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference

その他

その他2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013
オランダ
Amsterdam
期間7/6/137/10/13

    フィンガープリント

All Science Journal Classification (ASJC) codes

  • Genetics
  • Computational Mathematics

これを引用

Vargas, D. V., Takano, H., & Murata, J. (2013). Self organizing classifiers and niched fitness. : GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference (pp. 1109-1116). (GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/2463372.2463501