Comparative Evaluations of Evolutionary Computation with Elite Obtained in Reduced Dimensional Spaces - 九大コレクション | 九州大学附属図書館

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＜会議発表論文＞
Comparative Evaluations of Evolutionary Computation with Elite Obtained in Reduced Dimensional Spaces

作成者	作成者名 Pei, Yan 裴, 岩所属機関所属機関名 Graduate School of Design, Kyushu University 九州大学大学院芸術工学府
作成者	著者識別子 K002333 0000-0002-1431-9472 作成者名 Takagi, Hideyuki 髙木, 英行タカギ, ヒデユキ所属機関所属機関名 Faculty of Design, Kyushu University 九州大学大学院芸術工学研究院
本文言語	英語
出版者	IEEE
発行日	2011-11-30
収録物名	International Conference on Intelligent Networking and Collaborative Systems (INCoS)
巻	3
開始ページ	1
出版タイプ	Accepted Manuscript
アクセス権	open access
関連DOI	Proceedings, Third IEEE International Conference on Intelligent Networking and Collaborative Systems
関連DOI	https://doi.org/10.1109/INCoS.2011.66
関連URI	https://doi.org/10.1109/INCoS.2011.66
関連情報	Proceedings, Third IEEE International Conference on Intelligent Networking and Collaborative Systems
関連情報	https://doi.org/10.1109/INCoS.2011.66
概要	We propose an elite synthesis optimization strategy for accelerating evolutionary computation (EC) searches using elites obtained from a lower dimensional space. The method projects individuals onto n... one-dimensional spaces corresponding to each of the n searching parameter axes, approximates each landscape using Lagrange polynomial interpolation or power function least squares approximation, finds the best coordinate for the approximated shape, obtains the elite by combining the best n found coordinates, and uses the elite for the next generation of the EC. The advantage of this method is that the elite may be easily obtained thanks to their projection onto each one-dimensional space and that there is a higher possibility that the elite will be located near the global optimum. We conduct experimental tests to compare our proposed approaches with previous acceleration approaches using differential evolution and ten benchmark functions. The results demonstrate that the proposed method accelerates EC convergence significantly, especially in early generations.続きを見る
目次	Ⅰ.INTRODUCTION Ⅱ.ELITE SYNTHESIS OPTIMIZATION STRATEGY Ⅲ.EXPERIMENTAL EVALUATIONS Ⅳ.DISCUSSIONS Ⅴ.CONCLUSION AND FUTURE WORK

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INCoS2011	pdf	1.46 MB	385

詳細

レコードID	1808444
査読有無	査読有
主題	evolutionary computation
	fitness landscape
	convergence acceleration
	dimensionality reduction
	approximation
	approach
DOI	10.1109/INCoS.2011.66
注記	IEEE INCoS 2011 : Third IEEE International Conference on Intelligent Networking and Collaborative System : 30 November – 2 December 2011 : Fukuoka Institute of Technology (FIT), Fukuoka, Japan
登録日	2017.06.08
更新日	2021.10.06