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Abstract

One aspect that most of the current research on evolutionary multiobjective optimization (EMO) often disregards is the fact that the solution of a multiobjective optimization problem (MOP) really involves three stages: measurement, search, and decision making.

“In the real world we usually do not have a choice between satisfactory and optimal solutions, for we only rarely have a method of finding the optimum... We cannot, with practicable computational limits, generate all the admissible alternatives and compare their relative merits. Nor can we recognize the best alternative, even if we are fortunate enough to generate it early, until we have seen all of them. We satisfice by looking for alternatives in such a way that we can generally find an acceptable one after only moderate search.”

—Herbert Simon

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© 2002 Springer Science+Business Media New York

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Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B. (2002). Multi-Criteria Decision Making. In: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic Algorithms and Evolutionary Computation, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5184-0_8

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  • DOI: https://doi.org/10.1007/978-1-4757-5184-0_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4757-5186-4

  • Online ISBN: 978-1-4757-5184-0

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