### 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 |
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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 2013 → 7 10 2013 |

### 出版物シリーズ

名前 | GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference |
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### その他

その他 | 2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013 |
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国 | オランダ |

市 | Amsterdam |

期間 | 7/6/13 → 7/10/13 |

### フィンガープリント

### All Science Journal Classification (ASJC) codes

- Genetics
- Computational Mathematics

### これを引用

*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