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
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A similarity-based mating scheme for evolutionary multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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
Biobjective evolutionary and heuristic algorithms for intersection of geometric graphs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic algorithm for the personnel assignment problem with multiple objectives
Information Sciences: an International Journal
International Journal of Approximate Reasoning
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In this paper, we demonstrate advantages and disadvantages of an evolutionary multiobjective optimization (EMO) approach in comparison with a reference solution-based single-objective approach through computational experiments on multiobjective 0/1 knapsack problems. The main characteristic feature of the EMO approach is that no a priori information about the decision maker's preference is assumed. The EMO approach tries to find well-distributed trade-off solutions with a wide range of objective values as many as possible. A final solution is supposed to be chosen from the obtained trade-off solutions by the decision maker. On the other hand, the reference solution-based approach utilizes the information about the decision maker's preference in the form of a reference solution. We examine whether the EMO approach can find good trade-off solutions close to an arbitrarily given reference solution. Experimental results show that good solutions are not always obtained by the EMO approach. We also examine where the reference solution-based approach can find many trade-off solutions around the given reference solution. Experimental results show that many trade-off solutions can not be obtained even when an archive population of non-dominated solutions is stored in the reference solution-based approach. Based on these observations, we suggest a hybrid approach.