Evolutionary Algorithms in Management Applications:
Evolutionary Algorithms (EA) are powerful search and optimisation techniques inspired by the mechanisms of natural evolution. They imitate, on an abstract level, biological principles such as a population based approach, the inheritance of information, the variation of information via crossover/muta...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1995
|
| Ausgabe: | 1st ed. 1995 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | Evolutionary Algorithms (EA) are powerful search and optimisation techniques inspired by the mechanisms of natural evolution. They imitate, on an abstract level, biological principles such as a population based approach, the inheritance of information, the variation of information via crossover/mutation, and the selection of individuals based on fitness. The most well-known class of EA are Genetic Algorithms (GA), which have received much attention not only in the scientific community lately. Other variants of EA, in particular Genetic Programming, Evolution Strategies, and Evolutionary Programming are less popular, though very powerful too. Traditionally, most practical applications of EA have appeared in the technical sector. Management problems, for a long time, have been a rather neglected field of EA-research. This is surprising, since the great potential of evolutionary approaches for the business and economics domain was recognised in pioneering publications quite a while ago. John Holland, for instance, in his seminal book Adaptation in Natural and Artificial Systems (The University of Michigan Press, 1975) identified economics as one of the prime targets for a theory of adaptation, as formalised in his reproductive plans (later called Genetic Algorithms) |
| Beschreibung: | 1 Online-Ressource (XV, 379 p. 48 illus) |
| ISBN: | 9783642612176 |
| DOI: | 10.1007/978-3-642-61217-6 |
Internformat
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| 520 | |a Evolutionary Algorithms (EA) are powerful search and optimisation techniques inspired by the mechanisms of natural evolution. They imitate, on an abstract level, biological principles such as a population based approach, the inheritance of information, the variation of information via crossover/mutation, and the selection of individuals based on fitness. The most well-known class of EA are Genetic Algorithms (GA), which have received much attention not only in the scientific community lately. Other variants of EA, in particular Genetic Programming, Evolution Strategies, and Evolutionary Programming are less popular, though very powerful too. Traditionally, most practical applications of EA have appeared in the technical sector. Management problems, for a long time, have been a rather neglected field of EA-research. This is surprising, since the great potential of evolutionary approaches for the business and economics domain was recognised in pioneering publications quite a while ago. John Holland, for instance, in his seminal book Adaptation in Natural and Artificial Systems (The University of Michigan Press, 1975) identified economics as one of the prime targets for a theory of adaptation, as formalised in his reproductive plans (later called Genetic Algorithms) | ||
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| bvnumber | BV046872834 |
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| ctrlnum | (ZDB-2-SBE)978-3-642-61217-6 (OCoLC)903190906 (DE-599)BVBBV046872834 |
| dewey-full | 658.40301 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.40301 |
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| dewey-sort | 3658.40301 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| doi_str_mv | 10.1007/978-3-642-61217-6 |
| edition | 1st ed. 1995 |
| format | Electronic eBook |
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| index_date | 2024-07-03T15:15:38Z |
| indexdate | 2024-07-10T08:56:10Z |
| institution | BVB |
| isbn | 9783642612176 |
| language | English |
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| spelling | Evolutionary Algorithms in Management Applications edited by Jörg Biethahn, Volker Nissen 1st ed. 1995 Berlin, Heidelberg Springer Berlin Heidelberg 1995 1 Online-Ressource (XV, 379 p. 48 illus) txt rdacontent c rdamedia cr rdacarrier Evolutionary Algorithms (EA) are powerful search and optimisation techniques inspired by the mechanisms of natural evolution. They imitate, on an abstract level, biological principles such as a population based approach, the inheritance of information, the variation of information via crossover/mutation, and the selection of individuals based on fitness. The most well-known class of EA are Genetic Algorithms (GA), which have received much attention not only in the scientific community lately. Other variants of EA, in particular Genetic Programming, Evolution Strategies, and Evolutionary Programming are less popular, though very powerful too. Traditionally, most practical applications of EA have appeared in the technical sector. Management problems, for a long time, have been a rather neglected field of EA-research. This is surprising, since the great potential of evolutionary approaches for the business and economics domain was recognised in pioneering publications quite a while ago. John Holland, for instance, in his seminal book Adaptation in Natural and Artificial Systems (The University of Michigan Press, 1975) identified economics as one of the prime targets for a theory of adaptation, as formalised in his reproductive plans (later called Genetic Algorithms) Operations Research/Decision Theory IT in Business Computer Communication Networks Operations research Decision making Information technology Business—Data processing Computer communication systems Management (DE-588)4037278-9 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Management (DE-588)4037278-9 s Evolutionärer Algorithmus (DE-588)4366912-8 s DE-604 Biethahn, Jörg edt Nissen, Volker edt Erscheint auch als Druck-Ausgabe 9783642647499 Erscheint auch als Druck-Ausgabe 9783642612183 Erscheint auch als Druck-Ausgabe 9783540603825 https://doi.org/10.1007/978-3-642-61217-6 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Evolutionary Algorithms in Management Applications Operations Research/Decision Theory IT in Business Computer Communication Networks Operations research Decision making Information technology Business—Data processing Computer communication systems Management (DE-588)4037278-9 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4037278-9 (DE-588)4366912-8 (DE-588)4143413-4 |
| title | Evolutionary Algorithms in Management Applications |
| title_auth | Evolutionary Algorithms in Management Applications |
| title_exact_search | Evolutionary Algorithms in Management Applications |
| title_exact_search_txtP | Evolutionary Algorithms in Management Applications |
| title_full | Evolutionary Algorithms in Management Applications edited by Jörg Biethahn, Volker Nissen |
| title_fullStr | Evolutionary Algorithms in Management Applications edited by Jörg Biethahn, Volker Nissen |
| title_full_unstemmed | Evolutionary Algorithms in Management Applications edited by Jörg Biethahn, Volker Nissen |
| title_short | Evolutionary Algorithms in Management Applications |
| title_sort | evolutionary algorithms in management applications |
| topic | Operations Research/Decision Theory IT in Business Computer Communication Networks Operations research Decision making Information technology Business—Data processing Computer communication systems Management (DE-588)4037278-9 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Operations Research/Decision Theory IT in Business Computer Communication Networks Operations research Decision making Information technology Business—Data processing Computer communication systems Management Evolutionärer Algorithmus Aufsatzsammlung |
| url | https://doi.org/10.1007/978-3-642-61217-6 |
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