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
An empirical study on the effect of mating restriction on the search ability of EMO algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
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
IEEE Transactions on Evolutionary Computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rank based variation operators for genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Localization for solving noisy multi-objective optimization problems
Evolutionary Computation
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
Adaptive and assortative mating scheme for evolutionary multi-objective algorithms
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Recombination of similar parents in EMO algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A hybrid multiobjective evolutionary algorithm: Striking a balance with local search
Mathematical and Computer Modelling: An International Journal
General framework for localised multi-objective evolutionary algorithms
Information Sciences: an International Journal
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This paper proposes a new mating scheme for evolutionary multiobjective optimization (EMO), which simultaneously improves the convergence speed to the Pareto-front and the diversity of solutions. The proposed mating scheme is a two-stage selection mechanism. In the first stage, standard fitness-based selection is iterated for selecting a pre-specified number of candidate solutions from the current population. In the second stage, similarity-based tournament selection is used for choosing a pair of parents among the candidate solutions selected in the first stage. For maintaining the diversity of solutions, selection probabilities of parents are biased toward extreme solutions that are different from prototypical (i.e., average) solutions. At the same time, our mating scheme uses a mechanism where similar parents are more likely to be chosen for improving the convergence speed to the Pareto-front. Through computational experiments on multi-objective knapsack problems, it is shown that the performance of recently proposed well-known EMO algorithms (SPEA and NSGA-II) can be improved by our mating scheme.