Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
LCGA: Local Cultivation Genetic Algorithm For Multi-objective Optimization Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Localization for solving noisy multi-objective optimization problems
Evolutionary Computation
A similarity-based mating scheme for evolutionary multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Adaptive and assortative mating scheme for evolutionary multi-objective algorithms
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Exploiting comparative studies using criteria: generating knowledge from an analyst's perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Recombination of similar parents in EMO algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Local preference-inspired co-evolutionary algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
General framework for localised multi-objective evolutionary algorithms
Information Sciences: an International Journal
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This paper examines the effect of mating restriction on the search ability of EMO algorithms. First we propose a simple but flexible mating restriction scheme where a pair of similar (or dissimilar) individuals is selected as parents. In the proposed scheme, one parent is selected from the current population by the standard binary tournament selection. Candidates for a mate of the selected parent are winners of multiple standard binary tournaments. The selection of the mate among multiple candidates is based on the similarity (or dissimilarity) to the first parent. The strength of mating restriction is controlled by the number of candidates (i.e., the number of tournaments used for choosing candidates from the current population). Next we examine the effect of mating restriction on the search ability of EMO algorithms to find all Pareto-optimal solutions through computational experiments on small test problems using the SPEA and the NSGA-II. It is shown that the choice of dissimilar parents improves the search ability of the NSGA-II on small test problems. Then we further examine the effect of mating restriction using large test problems. It is shown that the choice of similar parents improves the search ability of the SPEA and the NSGA-II to efficiently find near Pareto-optimal solutions of large test problems. Empirical results reported in this paper suggest that the proposed mating restriction scheme can improve the performance of EMO algorithms for many test problems while its effect is problem-dependent and algorithm-dependent.