Hybrid Metaheuristics An Emerging Approach to Optimization /
Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as metaheuristics. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant co...
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| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edition: | 1st ed. 2008. |
| Series: | Studies in Computational Intelligence,
114 |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-540-78295-7 |
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| 001 | 978-3-540-78295-7 | ||
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| 245 | 1 | 0 | |a Hybrid Metaheuristics |h [electronic resource] : |b An Emerging Approach to Optimization / |c edited by Christian Blum, Andrea Roli, Michael Sampels. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2008. | |
| 300 | |a X, 290 p. |b online resource. | ||
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| 490 | 1 | |a Studies in Computational Intelligence, |x 1860-949X ; |v 114 | |
| 505 | 0 | |a Hybrid Metaheuristics: An Introduction -- Combining (Integer) Linear Programming Techniques and Metaheuristics for Combinatorial Optimization -- The Relation Between Complete and Incomplete Search -- Hybridizations of Metaheuristics With Branch & Bound Derivates -- Very Large-Scale Neighborhood Search: Overview and Case Studies on Coloring Problems -- Hybrids of Constructive Metaheuristics and Constraint Programming: A Case Study with ACO -- Hybrid Metaheuristics for Packing Problems -- Hybrid Metaheuristics for Multi-objective Combinatorial Optimization -- Multilevel Refinement for Combinatorial Optimisation: Boosting Metaheuristic Performance. | |
| 520 | |a Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as metaheuristics. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization. In recent years it has become evident that a skilled combination of a metaheuristic with other optimization techniques, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility. This is because hybrid metaheuristics combine their advantages with the complementary strengths of, for example, more classical optimization techniques such as branch and bound or dynamic programming. The authors involved in this book are among the top researchers in their domain. The book is intended both to provide an overview of hybrid metaheuristics to novices of the field, and to provide researchers from the field with a collection of some of the most interesting recent developments. | ||
| 650 | 0 | |a Applied mathematics. | |
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Mathematical and Computational Engineering. |0 https://scigraph.springernature.com/ontologies/product-market-codes/T11006 |
| 650 | 2 | 4 | |a Artificial Intelligence. |0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
| 700 | 1 | |a Blum, Christian. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Roli, Andrea. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Sampels, Michael. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 830 | 0 | |a Studies in Computational Intelligence, |x 1860-949X ; |v 114 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-540-78295-7 |
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