Evolutionary Optimization:
Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques...
Gespeichert in:
| Weitere beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
Springer US
2002
|
| Ausgabe: | 1st ed. 2002 |
| Schriftenreihe: | International Series in Operations Research & Management Science
48 |
| Schlagwörter: | |
| Links: | https://doi.org/10.1007/b101816 https://doi.org/10.1007/b101816 |
| Zusammenfassung: | Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization |
| Umfang: | 1 Online-Ressource (XIV, 418 p) |
| ISBN: | 9780306480416 |
| DOI: | 10.1007/b101816 |
Internformat
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| 520 | |a Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization | ||
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Datensatz im Suchindex
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| dewey-full | 658.4092 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
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| discipline | Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1007/b101816 |
| edition | 1st ed. 2002 |
| format | Electronic eBook |
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| indexdate | 2024-12-20T19:03:24Z |
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| spelling | Evolutionary Optimization edited by Ruhul Sarker, Masoud Mohammadian, Xin Yao 1st ed. 2002 New York, NY Springer US 2002 1 Online-Ressource (XIV, 418 p) txt rdacontent c rdamedia cr rdacarrier International Series in Operations Research & Management Science 48 Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization Business Strategy/Leadership Operations Research/Decision Theory Optimization Calculus of Variations and Optimal Control; Optimization Artificial Intelligence Leadership Operations research Decision making Mathematical optimization Calculus of variations Artificial intelligence Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Optimierung (DE-588)4043664-0 s Evolutionärer Algorithmus (DE-588)4366912-8 s DE-604 Sarker, Ruhul edt Mohammadian, Masoud edt Xin Yao edt Erscheint auch als Druck-Ausgabe 9781475775709 Erscheint auch als Druck-Ausgabe 9780792376545 Erscheint auch als Druck-Ausgabe 9781475775693 https://doi.org/10.1007/b101816 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Evolutionary Optimization Business Strategy/Leadership Operations Research/Decision Theory Optimization Calculus of Variations and Optimal Control; Optimization Artificial Intelligence Leadership Operations research Decision making Mathematical optimization Calculus of variations Artificial intelligence Evolutionärer Algorithmus (DE-588)4366912-8 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4043664-0 (DE-588)4143413-4 |
| title | Evolutionary Optimization |
| title_auth | Evolutionary Optimization |
| title_exact_search | Evolutionary Optimization |
| title_full | Evolutionary Optimization edited by Ruhul Sarker, Masoud Mohammadian, Xin Yao |
| title_fullStr | Evolutionary Optimization edited by Ruhul Sarker, Masoud Mohammadian, Xin Yao |
| title_full_unstemmed | Evolutionary Optimization edited by Ruhul Sarker, Masoud Mohammadian, Xin Yao |
| title_short | Evolutionary Optimization |
| title_sort | evolutionary optimization |
| topic | Business Strategy/Leadership Operations Research/Decision Theory Optimization Calculus of Variations and Optimal Control; Optimization Artificial Intelligence Leadership Operations research Decision making Mathematical optimization Calculus of variations Artificial intelligence Evolutionärer Algorithmus (DE-588)4366912-8 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Business Strategy/Leadership Operations Research/Decision Theory Optimization Calculus of Variations and Optimal Control; Optimization Artificial Intelligence Leadership Operations research Decision making Mathematical optimization Calculus of variations Artificial intelligence Evolutionärer Algorithmus Optimierung Aufsatzsammlung |
| url | https://doi.org/10.1007/b101816 |
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