Titlebook: Evolutionary Multi-Criterion Optimization; 8th International Co António Gaspar-Cunha,Carlos Henggeler Antunes,Carl Conference proceedings 2

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https://doi.org/10.1007/978-3-319-23895-1ssignment problem using a set of weight vectors uniformly scattered. Our approach adopts uniform design to obtain the set of weights and Kuhn-Munkres’ (Hungarian) algorithm to solve the assignment problem. Differential evolution is used as our search engine, giving rise to the so-called Hungarian Di

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https://doi.org/10.1057/978-1-349-94892-5ion. To evaluate these algorithms, performance metrics either require the knowledge of the true Pareto-optimal solutions or, are ad-hoc and heuristic based. In this paper, we suggest a KKT proximity measure (KKTPM) that can provide an estimate of the proximity of a set of trade-off solutions from th

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Mário Henrique Ogasavara,Gilmar Masieroot only as a quality measure for comparing final results of multi-objective evolutionary algorithms (MOEAs), but also as a selection operator (it is, for example, very suitable for .). However, it has one serious drawback: computing the exact hypervolume is highly costly. The best known algorithms t

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https://doi.org/10.1057/9781137316257rmance indicators evaluate the quality of an obtained solution set in comparison with a pre-specified reference point set. Both indicators are based on the distance between a solution and a reference point. The Euclidean distance in an objective space is usually used for distance calculation. Our id

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