Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparison between lamarckian and baldwinian repair on multiobjective 0/1 knapsack problems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid
International Journal of Data Mining and Bioinformatics
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In multi-objective optimization, it is important that the obtained solutions are high quality regarding accuracy, uniform distribution, and broadness. Of these qualities, we focused on accuracy and broadness of the solutions and proposed a search strategy. Since it is difficult to improve both convergence and broadness of the solutions at the same time in a multi-objective GA search, we considered to converge the solutions first and then broaden them in the proposed search strategy by dividing the search into two search stages. The first stage is to improve convergence of the solutions, and a reference point specified by a decision maker is adopted in this search. In the second stage, the solutions are broadened using the Distributed Cooperation Scheme. From the results of the numerical experiment, we found that the proposed search strategy is capable of deriving broader solutions than conventional multi-objective GA with equivalent accuracy.