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遺伝的プログラミングでの多様性維持について
http://hdl.handle.net/10191/21944
http://hdl.handle.net/10191/2194495c00389-2771-4407-b190-d5d4cc96b264
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
21_3_219-230.pdf (1.6 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2013-04-19 | |||||
| タイトル | ||||||
| タイトル | 遺伝的プログラミングでの多様性維持について | |||||
| タイトル | ||||||
| タイトル | 遺伝的プログラミングでの多様性維持について | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | genetic programming | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | diversity maintenance | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | fitness sharing | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_6501 | |||||
| タイプ | journal article | |||||
| その他のタイトル | ||||||
| その他のタイトル | Diversity Maintenance in Genetic Programming | |||||
| 著者 |
元木, 達也
× 元木, 達也× 沼口, 靖 |
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| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 40578 | |||||
| 姓名 | Motoki, Tatsuya | |||||
| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 40581 | |||||
| 姓名 | Numaguchi, Yasushi | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | This paper is motivated by an experimental result that better performing genetic programming runs tend to have higher phenotypic diversity. To maintain phenotypic diversity, we apply implicit fitness sharing and its variant, called unfitness multiplying. To apply these methods to problems in which individuals have infinite kinds of possible behaviours, we classify posible behaviours into 50 achievement levels, and assign a reward or a penalty to each level. In implicit fitness sharing a reward is shared out among individuals with the same achievement level, and in unfitness multiplying a penalty is multiplied by the number of individuals with the same level and is distributed to related individuals. Five benchmark problems (11-multiplexer, sextic polynomial, four-sine, intertwined spiral, and artificial ant problems) are used to illustrate the effect of the methods. The results show that our methods clearly promote diversity and lead population to a smooth frequency distribution of achievement levels, and that our methods usually perform better than the original implicit fitness sharing on success rate and the best (raw) fitness. We also observe that the unfitness multiplying makes a quite different ranking over individuals than the one by the implicit fitness sharing. | |||||
| 書誌情報 |
人工知能学会論文誌 en : 人工知能学会論文誌 巻 21, 号 3, p. 219-230, 発行日 2006-02 |
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| 出版者 | ||||||
| 出版者 | 人工知能学会 | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 13460714 | |||||
| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA11579226 | |||||
| 著者版フラグ | ||||||
| 値 | publisher | |||||
