Titlebook: Evolutionary Multi-Criterion Optimization; Third International Carlos A. Coello Coello,Arturo Hernández Aguirre,E Conference proceedings 2

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Experiential Learning for Entrepreneurship often be conveniently formulated as multiobjective optimization problems, these often comprise a relatively large number of objectives. Such problems pose new challenges for algorithm design, visualisation and implementation. Each of these three topics is addressed. Progressive articulation of desi

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https://doi.org/10.1007/978-3-030-82087-9. In this paper, we suggest an optimization procedure which specializes in solving multi-objective, multi-global problems. The algorithm is carefully designed so as to degenerate to efficient algorithms for solving other simpler optimization problems, such as single-objective uni-global problems, si

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Kate Hudgins,Steven William Durosts (EMOA). The idea to use this measure for selection is self-evident. A steady-state EMOA will be devised, that combines concepts of non-dominated sorting with a selection operator based on the hypervolume measure. The algorithm computes a well distributed set of solutions with bounded size thereby

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Dionysis Anemogiannis,Angelos Theocharisintenance and population-based Pareto ranking to achieve good approximations of the Pareto front. While it is certainly true that these techniques have been effective, they come at a significant complexity cost that ultimately limits their application to complex problems. This paper proposes a new m

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On the Nature of Ball Lightningl frontier, representing the best possible objective values. However, in practice, users may not always be interested in finding the global best solutions, particularly if these solutions are quite sensitive to the variable perturbations which cannot be avoided in practice. In such cases, practition