Classification Scheme of Multi-objective Estimation of Distribution Algorithms

被引：0
作者：
Mendoza-Gonzalez, Alfredo [1 ]
Ponce-de-Leon, Eunice [1 ]
Diaz-Diaz, Elva [1 ]
机构：
[1] Autonomous Univ Aguascalientes, Intelligent Comp Dept, Aguascalientes, Ags, Mexico
来源：
2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2013年
关键词：
Estimation of Distribution Algorithms; Evolutionary Algorithms; Multi-objective optimization;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A variety of Estimation of Distribution Algorithms for multi-objective optimization (MOEDAs) has been reported, each of them with its own characteristics and techniques in their optimization process. In this research we present a classification scheme for these algorithms, based on ten characteristics: domain of the variables, relationships between the variables, probabilistic graphical model, estimation approach, restriction support, problem handling, sorting method, individuals' handling, selection approach, and replacement approach. These characteristics were extracted by analyzing all the 24 MOEDAs reported in the literature. The scheme presented here helps to identify the methods and techniques used in each algorithm, also, a useful method for the analysis of the optimization process of an EDA is proposed. This paper includes a brief analysis of the influence in the results of applying different selection/replacement percentages.
引用
收藏
页码：3051 / 3057
页数：7
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