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Record Nr. |
UNINA9910623995603321 |
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Autore |
Taillard Éric D |
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Titolo |
Design of Heuristic Algorithms for Hard Optimization : With Python Codes for the Travelling Salesman Problem / / by Éric D. Taillard |
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Pubbl/distr/stampa |
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Cham, : Springer Nature, 2023 |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (XV, 287 p. 1 illus.) |
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Collana |
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Graduate Texts in Operations Research, , 2662-6020 |
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Disciplina |
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Soggetti |
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Operations research |
Mathematical optimization |
Mathematics—Data processing |
Algorithms |
Artificial intelligence |
Operations Research and Decision Theory |
Optimization |
Computational Mathematics and Numerical Analysis |
Computational Science and Engineering |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Part I: Combinatorial Optimization, Complexity Theory and Problem Modelling -- 1. Elements of Graphs and Complexity Theory -- 2. A Short List of Combinatorial Optimization Problems -- 3. Problem Modelling -- Part II: Basic Heuristic Techniques -- 4. Constructive Methods -- 5. Local Search -- 6. Decomposition Methods -- Part III: Popular Metaheuristics -- 7. Randomized Methods -- 8. Construction Learning -- 9. Local Search Learning -- 10. Population Management -- 11. Heuristics Design -- 12. Codes. |
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Sommario/riassunto |
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This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the |
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techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content. |
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