Intelligence Semantics

Hybrid Metaheuristics: An Emerging Approach to Optimization by Christian Blum, Andrea Roli, Michael Sampels

By Christian Blum, Andrea Roli, Michael Sampels

Optimization difficulties are of serious significance throughout a wide variety of fields. they are often tackled, for instance, by means of approximate algorithms similar to metaheuristics. This e-book is meant either to supply an outline of hybrid metaheuristics to newcomers of the sector, and to supply researchers from the sphere with a set of a few of the main attention-grabbing fresh advancements. The authors enthusiastic about this publication are one of the most sensible researchers of their area.

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Major structural classification of exact/metaheuristic combinations according to [67]. different collaborative schemes consisting of two of the above mentioned algorithm classes. A taxonomy on hybrid metaheuristics in general has been proposed by Talbi [82]. Various hybridization schemes involving in particular evolutionary algorithms (EAs) are described by Cotta [19]. El-Abd and Kamel [26] particularly addressed cooperative parallel architectures. Raidl [75] tries to unify previous classifications and taxonomies of hybrid metaheuristics and primarily distinguishes (a) the type of algorithms that are hybridized, (b) the level of hybridization (high- or low-level), (c) the order of execution (batch, interleaved, or parallel), and (d) the control strategy (integrative or collaborative).

The subproblem to be processed next in case of guided dives is always the one in which the branching variable is allowed to take the value it has in a current incumbent solution. Diving is therefore biased towards the neighborhood of the given incumbent. Instead of performing only a single dive at the beginning, guided dives are repeatedly applied in regular intervals during the whole optimization. While this strategy is trivial to implement, experimental results indicate significant advantages over standard node selection strategies.

37. G. B. Fogel, V. W. Porto, D. G. Weekes, D. B. Fogel, R. H. Griffey, J. A. McNeil, E. Lesnik, D. J. Ecker, and R. Sampath. Discovery of RNA structural elements using evolutionary computation. Nucleic Acids Research, 30(23):5310–5317, 2002. 38. L. J. Fogel. Toward inductive inference automata. In Proceedings of the International Federation for Information Processing Congress, pages 395–399, Munich, 1962. 39. L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution.

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