Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey Export

@proceedings{citeulike:4665563, abstract = {This paper presents a review of preference incorporation in Multi-Objective Evolutionary Algorithms (MOEA). The incorporation of preference in Evolutionary Multi-objective Optimization (EMO) promotes better decisionmaking. Introducing preference in MOEAs increases the specificity of selection, leading to solutions which are of higher relevance to the Decision Maker(s). When many objectives are involved, a MOEA based on pure Pareto-optimality criterion may not achieve meaningful search. The incorporation of preference addresses this concern. The incorporation of preference is difficult because of uncertainties arising from lack of prior problem knowledge and fuzziness of human preference. Further, decision making is a complex and ill-defined process which at times could not be mathematically characterized. These concerns must be addressed in the incorporation of preference.}, author = {Rachmawati, L. and Srinivasan, D.}, booktitle = {Evolutionary Computation, 2006. CEC 2006. IEEE Congress on}, citeulike-article-id = {4665563}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/CEC.2006.1688414}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1688414}, doi = {10.1109/CEC.2006.1688414}, journal = {Evolutionary Computation, 2006. CEC 2006. IEEE Congress on}, keywords = {fitness, multiobjective}, pages = {962--968}, posted-at = {2009-05-28 12:26:13}, priority = {2}, title = {Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey}, url = {http://dx.doi.org/10.1109/CEC.2006.1688414}, year = {2006} }

TY - CONF ID - citeulike:4665563 L3 - citeulike-article-id:4665563 N2 - This paper presents a review of preference incorporation in Multi-Objective Evolutionary Algorithms (MOEA). The incorporation of preference in Evolutionary Multi-objective Optimization (EMO) promotes better decisionmaking. Introducing preference in MOEAs increases the specificity of selection, leading to solutions which are of higher relevance to the Decision Maker(s). When many objectives are involved, a MOEA based on pure Pareto-optimality criterion may not achieve meaningful search. The incorporation of preference addresses this concern. The incorporation of preference is difficult because of uncertainties arising from lack of prior problem knowledge and fuzziness of human preference. Further, decision making is a complex and ill-defined process which at times could not be mathematically characterized. These concerns must be addressed in the incorporation of preference. JF - Evolutionary Computation, 2006. CEC 2006. IEEE Congress on EP - 968 TI - Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey T2 - Evolutionary Computation, 2006. CEC 2006. IEEE Congress on SP - 962 KW - fitness KW - multiobjective AU - Rachmawati, L AU - Srinivasan, D PY - 2006/// UR - http://dx.doi.org/10.1109/CEC.2006.1688414 ER -