Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4633423 | Applied Mathematics and Computation | 2009 | 10 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Jing Xiao, YuPing Yan, Jun Zhang,