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
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
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
IEEE Transactions on Evolutionary Computation
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We focus on the handling of overlapping solutions in evolutionary multiobjective optimization (EMO) algorithms. First we show that there exist a large number of overlapping solutions in each population when EMO algorithms are applied to multiobjective combinatorial optimization problems with only a few objectives. Next we implement three strategies to handle overlapping solutions. One strategy is the removal of overlapping solutions in the objective space. In this strategy, overlapping solutions in the objective space are removed during the generation update phase except for only a single solution among them. As a result, each solution in the current population has a different location in the objective space. Another strategy is to remove overlapping solutions so that each solution in the current population has a different location in the decision space. The other strategy is the modification of Pareto ranking where overlapping solutions in the objective space are allocated to different fronts. As a result, each solution in each front has a different location in the objective space. Effects of each strategy on the performance of the NSGA-II algorithm are examined through computational experiments on multiobjective 0/1 knapsack problems, multiobjective flowshop scheduling problems, and multiobjective fuzzy rule selection problems.