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.
Being able to find P true does not completely solve an MOP. The decision maker (DM) still has to choose a single solution out of this set. The process of selecting a single solution is not trivial. In fact, there is a set of methodologies regarding how and when to incorporate decisions from the DM into the search process.
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(2007). Multi-Criteria Decision Making. In: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36797-2_9
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DOI: https://doi.org/10.1007/978-0-387-36797-2_9
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