A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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The paper presents a comparative experiment with four multiple objective evolutionary algorithms on a real life combinatorial optimization problem. The test problem corresponds to the design of a distribution system. The experiment compares performance of a Pareto ranking based multiple objective genetic algorithm (Pareto GA), multiple objective multiple start local search (MOMSLS), multiple objective genetic local search (MOGLS) and an extension of Pareto GA involving local search (Pareto GLS). The results of the experiment clearly indicate that the methods hybridizing recombination and local search operators by far outperform methods that use one of the operators alone. Furthermore, MOGLS outperforms Pareto GLS.