Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
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Currently there exist various cost-assignment schemes that perform the necessary scalarization of the objective values when applied to a multi-objective optimization problem. Of course, the final decision depends highly on the nature of the problem but given the multiplicity of the schemes combined with the fact that what the user ultimately needs is a single compromise solution it is evident that elaborating the selection of the method is not a trivial task. This paper intends to address this problem by extending the benchmarks of optimality and reach time given in [1] to mutliobjective optimization problems. A number of existing cost-assignment schemes are evaluated using such benchmarks.