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
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Comparing a coevolutionary genetic algorithm for multiobjective optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
IEEE Transactions on Evolutionary Computation
Hype: An algorithm for fast hypervolume-based many-objective optimization
Evolutionary Computation
Preference-driven co-evolutionary algorithms show promise for many-objective optimisation
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Local preference-inspired co-evolutionary algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Preference-inspired co-evolutionary algorithm (PICEA) is a novel class of multi-objective evolutionary algorithm. In PICEA, the usual candidate solutions are guided toward the Pareto optimal front by co-evolving a set of decision maker preferences during the search process. PICEA-g is one realization of PICEAs in which goal vectors are taken as preferences. This study points out one limitation of this method -the obtained solutions are distributed unevenly along the Pareto optimal front. To handle this limitation, an improved fitness assignment method is proposed in which the density information of the solutions is considered. Experimental results, in terms of the selected performance metrics, show this improved fitness assignment method is effective.