Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2003.822958
Title: Incremental multiple objective genetic algorithms
Authors: Chen, Q.
Guan, S.-U. 
Keywords: Incremental problem solving
Multiobjective genetic algorithm
Multiobjective optimization
Multiobjective problems
Vector optimization
Issue Date: Jun-2004
Source: Chen, Q., Guan, S.-U. (2004-06). Incremental multiple objective genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 (3) : 1325-1334. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2003.822958
Abstract: This paper presents a new genetic algorithm approach to multiobjective optimization problems-incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objective population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experimental results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/56313
ISSN: 10834419
DOI: 10.1109/TSMCB.2003.822958
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