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HPBILc: A histogram-based EDA for continuous optimization
- Authors
- Xiao, Jing; Yan, YuPing; Zhang, Jun
- Issue Date
- Oct-2009
- Publisher
- Elsevier BV
- Keywords
- Histogram probabilistic model; Estimation of distribution algorithms; Continuous optimization
- Citation
- Applied Mathematics and Computation, v.215, no.3, pp 973 - 982
- Pages
- 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- Applied Mathematics and Computation
- Volume
- 215
- Number
- 3
- Start Page
- 973
- End Page
- 982
- ISSN
- 0096-3003
1873-5649
- Abstract
- Designing different estimation of distribution algorithms for continuous optimization is a recent emerging focus in the evolutionary computation field. This paper proposes an improved population-based incremental learning algorithm using histogram probabilistic model for continuous optimization. Histogram models are advantageous in describing the solution distribution of complex and multimodal continuous problems. The algorithm utilizes the sub-dividing strategy to guarantee the accuracy of optimal solutions. Experimental results show that the proposed algorithm is effective and it obtains better performance than the fast evolutionary programming (FEP) and those newly published EDAs in most test functions. (C) 2009 Elsevier Inc. All rights reserved.
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Related Researcher
- ZHANG, Jun
- ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)