An Overview of Evolutionary Algorithms in Multiobjective Optimization

作     者：Fonseca, Carlos M. Fleming, Peter J. 

作者机构：Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England 

出 版 物：《EVOLUTIONARY COMPUTATION》 (调优计算)

年 卷 期：1995年第3卷第1期

页      面：1-16页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Junta Nacional de Investigagcao Cientifica e Tecnologica, Portugal [BD/1595/91-IA] U.K. Engineering and Physical Sciences Research Council [GR/J70857] 

主　　题：evolutionary algorithms multiobjective optimization fitness assignment search strategies 

摘      要：The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and/or possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations.