フルテキストファイル
著者
Ohki, Makoto 鳥取大学工学研究科情報エレクトロニクス専攻電気電子工学講座 研究者総覧 KAKEN
キーワード
Many-objective genetic programming
Partial sampling
Tree structural distance
Pareto partial dominance
Subset size scheduling
Elimination of duplicates
抄録
This paper describes a technique on an optimization of tree-structure data by of multi-objective evolutionary algorithm, or multi-objective genetic programming. GP induces bloat of the tree structure as one of the major problem. The cause of bloat is that the tree structure obtained by the crossover operator grows bigger and bigger but its evaluation does not improve. To avoid the risk of bloat, a partial sampling operator is proposed as a mating operator. The size of the tree and a structural distance are introduced into the measure of the tree-structure data as the objective functions in addition to the index of the goodness of tree structure. GP is defined as a three-objective optimization problem. SD is also applied for the ranking of parent individuals instead to the crowding distance of the conventional NSGA-II. When the index of the goodness of tree-structure data is two or more, the number of objective functions in the above problem becomes four or more. We also propose an effective many-objective EA applicable to such the many-objective GP. We focus on NSGA-II based on Pareto partial dominance (NSGA-II-PPD). NSGA-II-PPD requires beforehand a combination list of the number of objective functions to be used for Pareto partial dominance (PPD). The contents of the combination list greatly influence the optimization result. We propose to schedule a parameter r meaning the subset size of objective functions for PPD and to eliminate individuals created by the mating having the same contents as the individual of the archive set.
出版者
Springer International Publishing
資料タイプ
学術雑誌論文
外部リンク
ISSN
25233971
掲載誌名
SN Applied Sciences
1
3
開始ページ
207
終了ページ
220
発行日
2019-02-04
出版者DOI
著者版フラグ
著者版
著作権表記
© Springer Nature Switzerland AG 2019
掲載情報
Ohki, M. Multi-objective genetic programming with partial sampling and its extension to many-objective. SN Appl. Sci. (2019) 1: 207. https://doi.org/10.1007/s42452-019-0208-y. This is a post-peer-review, pre-copyedit version of an article published in SN Applied Sciences. The final authenticated version is available online at: http://dx.doi.org/10.1007/s42452-019-0208-y
部局名
工学部・工学研究科
言語
英語