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Multi-objective Evolutionary Algorithms (MOEAs) always approximate a set of optimal solutions. This set is required to be well spread and uniformly covering wide area of the Pareto-optimal front. In practical context, the Decision Maker (DM) usually chooses solutions in the middle of objective space, where the surface bulges out the most. Such solutions are called “knee solutions”. They are the most...
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