Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Experimental Methods for the Analysis of Optimization Algorithms
Experimental Methods for the Analysis of Optimization Algorithms
Hype: An algorithm for fast hypervolume-based many-objective optimization
Evolutionary Computation
Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks
Journal of Network and Computer Applications
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-Pareto-Ranking evolutionary algorithm
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
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This paper extends an elitist multi-objective evolutionary algorithm, named GAME, based on several Pareto fronts corresponding to various fitness definitions. An additional operator is defined to create an adaptive version of this algorithm, called aGAME. This new operator alternates different modes of exploration of the search space all through an aGAME execution. Mode switching is controlled according to the values of two performance indicators, in order to maintain a good compromise between the quality and diversity of the returned solutions. aGAME is compared with the previous version (GAME) and with the three best-ranking algorithms of the CEC 2009 competition, using seven bi-objective benchmarks and the rules of this competition. This experimental comparison shows that aGAME outperforms these four algorithms, which validates both the efficiency of the proposed dynamic adaptive operator and the algorithm performance.