HPBILc: A histogram-based EDA for continuous optimization |
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Authors: | Jing Xiao YuPing Yan Jun Zhang |
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Institution: | aSchool of Information Science and Technology, Sun Yat-sen University, Guangzhou 510275, China |
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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. |
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Keywords: | Histogram probabilistic model Estimation of distribution algorithms Continuous optimization |
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