Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd 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
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Analysis on population size and neighborhood recombination on many-objective optimization
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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In this work, we analyze genetic diversity of Pareto optimal solutions (POS) and study effective crossover operators in evolutionary many-objective optimization. First we examine the diversity of genes in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that genes in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point crossover, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators Controlling the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEAε+ and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with n={100,250,500,750,1000} items (bits) and m={2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for m≥4 objectives and that their effectiveness becomes larger as the number of objectives m increases.