Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
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
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This paper presents a new kind of MOEA, namely DMOGA (Density based Multi-Objective Genetic Algorithm). After discussing the influence function and the density function, we employ density of a solution point as its fitness in order to make the DMOGA perform well on diversity. And then, we extend our discussions to fitness assignment and computation, pruning procedure when the non-dominated set is bigger than the size of evolutionary population, and selection from the environmental selection population. To make DMOGA more efficient, we propose to construct the non-dominated set with the Dealer's Principle. We compare our DMOGA with two popular MOEAs, and the experimental results are satisfactory.