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A scalable multi-objective test problem toolkit. (English) Zbl 1109.68603
Coello Coello, Carlos A. (ed.) et al., Evolutionary multi-criterion optimization. Third international conference, EMO 2005, Guanajuato, Mexico, March 9–11, 2005. Proceedings. Berlin: Springer (ISBN 3-540-24983-4/pbk). Lecture Notes in Computer Science 3410, 280-295 (2005).
Summary: This paper presents a new toolkit for creating scalable multi-objective test problems. The WFG Toolkit is flexible, allowing characteristics such as bias, multi-modality, and non-separability to be incorporated and combined as desired. A wide variety of Pareto optimal geometries are also supported, including convex, concave, mixed convex/concave, linear, degenerate, and disconnected geometries.
All problems created by the WFG Toolkit are well defined, are scalable with respect to both the number of objectives and the number of parameters, and have known Pareto optimal sets. Nine benchmark multi-objective problems are suggested, including one that is both multi-modal and non-separable, an important combination of characteristics that is lacking among existing (scalable) multi-objective problems.
For the entire collection see [Zbl 1069.68002].

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming
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