Interactive multiobjective optimization system WWW-NIMBUS on the internet
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
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In this paper a new evolutionary algorithm is described for multi-objective optimization. The new method handles non-linear objective functions and constraints and supports the decision-maker with an estimation of the Pareto set. This cluster-based method applies the Pareto-dominance principle. It approximates the Pareto set with the prototypes for each cluster and alternative prototypes as secondary population. The non-dominated set is continuously being up-dated: based on the Pareto ranking, the poorest clusters are regularly deleted, and the new ones are set.The method solves the usual test problems with a satisfactory level of accuracy.