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
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
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
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Journal of Global Optimization
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Recently, many genetic algorithms (GAs) have been developed as an approximate method to generate Pareto frontier (the set of Pareto optimal solutions) to multi-objective optimization problem. In multi-objective GAs, there are two important problems : how to assign a fitness for each individual, and how to make the diversified individuals. In order to overcome those problems, this paper suggests a new multi-objective GA using generalized data envelopment analysis (GDEA). Through numerical examples, the paper shows that the proposed method using GDEA can generate well-distributed as well as well-approximated Pareto frontiers with less number of function evaluations.