An adaptive niching EDA based on clustering analysis

Benhui Chen, Jinglu Hu

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

Estimation of Distribution Algorithms (EDAs) still suffer from the drawback of premature convergence for solving the optimization problems with irregular and complex multimodal landscapes. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first is the continuous EDA based on single Gaussian probabilistic model to solve two benchmark functional multimodal optimization problems. The second is a real complicated discrete EDA optimization problem, the protein 3-D HP model based on k-order Markov probabilistic model. The experiment studies demonstrate that the proposed adaptive niching EDA is an efficient method.

本文言語English
ホスト出版物のタイトル2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOI
出版ステータスPublished - 2010 12 1
イベント2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain
継続期間: 2010 7 182010 7 23

出版物シリーズ

名前2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
CountrySpain
CityBarcelona
Period10/7/1810/7/23

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Applied Mathematics

フィンガープリント 「An adaptive niching EDA based on clustering analysis」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル