Classification scheme of multi-objective Estimation of Distribution Algorithms

Abstract

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.