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C. Audet et al.
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A. E. Eiben et al.
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D Huang et al.
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Charles Audet et al.
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Fine-tuning of algorithms using fractional experimental designs and local search
B Adenso-Diaz et al.
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A fast and elitist multiobjective genetic algorithm: NSGA-II
K Deb et al.
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M Pelikan et al.
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Completely derandomized self-adaptation in evolution strategies
N Hansen et al.
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SP Coy et al.
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