Histogram-Based Estimation of Distribution Algorithm:A Competent Method for Continuous Optimization  

作　　者：丁楠 周树德 孙增圻 

机构地区：[1]Department of Electronic Engineering,Tsinghua University [2]Department of Computer Science and Technology,Tsinghua University

出　　处：《Journal of Computer Science & Technology》2008年第1期35-43,共9页计算机科学技术学报（英文版）

基　　金：This work is funded by the National Grand Fundamental Research 973 Program of China(Grant No.G2002cb312205).

摘　　要：Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. This paper utilizes histogram probabilistic model to describe the distribution of population and to generate promising solutions. The advantage of histogram model, its intrinsic multimodality, makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram model more efficiently explore and exploit the search space, several strategies are brought into the algorithms： the surrounding effect reduces the population size in estimating the model with a certain number of the bins and the shrinking strategy guarantees the accuracy of optimal solutions. Furthermore, this paper shows that histogram-based EDA （Estimation of distribution algorithm） can give comparable or even much better performance than those predominant EDAs based on Gaussian models.Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. This paper utilizes histogram probabilistic model to describe the distribution of population and to generate promising solutions. The advantage of histogram model, its intrinsic multimodality, makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram model more efficiently explore and exploit the search space, several strategies are brought into the algorithms： the surrounding effect reduces the population size in estimating the model with a certain number of the bins and the shrinking strategy guarantees the accuracy of optimal solutions. Furthermore, this paper shows that histogram-based EDA （Estimation of distribution algorithm） can give comparable or even much better performance than those predominant EDAs based on Gaussian models.

关 键 词：evolutionary algorithm estimation of distribution algorithm histogram probabilistic model surrounding effect shrinking strategy 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]