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Evolutionary and Swarm Intelligence Algorithms
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| Personen und Körperschaften: | , , |
|---|---|
| Titel: | Evolutionary and Swarm Intelligence Algorithms/ edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal |
| Format: | E-Book |
| Sprache: | Englisch |
| veröffentlicht: |
Cham
Springer
2019
|
| Gesamtaufnahme: |
Studies in Computational Intelligence Springer eBook Collection SpringerLink |
| Schlagwörter: | |
| Quelle: | Verbunddaten SWB |
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| 520 | |a This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike | ||
| 520 | |a Swarm and Evolutionary Computation -- Particle Swarm Optimization -- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization -- Spider Monkey Optimization Algorithm -- Genetic Algorithm and Its Advances in Embracing Memetics -- Constrained Multi-Objective Evolutionary Algorithm -- Genetic Programming for Classification and Feature Selection -- Genetic Programming for Job Shop Scheduling -- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems | ||
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| 700 | 1 | |a Bansal, Jagdish Chand |e HerausgeberIn |4 edt | |
| 700 | 1 | |a Singh, Pramod Kumar |e HerausgeberIn |4 edt | |
| 700 | 1 | |a Pal, Nikhil R. |e HerausgeberIn |0 (DE-627)1254724710 |0 (DE-576)184724716 |4 edt | |
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| contents | This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike, Swarm and Evolutionary Computation -- Particle Swarm Optimization -- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization -- Spider Monkey Optimization Algorithm -- Genetic Algorithm and Its Advances in Embracing Memetics -- Constrained Multi-Objective Evolutionary Algorithm -- Genetic Programming for Classification and Feature Selection -- Genetic Programming for Job Shop Scheduling -- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems |
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| spelling | Evolutionary and Swarm Intelligence Algorithms edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal, Cham Springer 2019, Online-Ressource (X, 190 p. 57 illus., 21 illus. in color, online resource), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Studies in Computational Intelligence 779, Springer eBook Collection, SpringerLink Bücher, This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike, Swarm and Evolutionary Computation -- Particle Swarm Optimization -- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization -- Spider Monkey Optimization Algorithm -- Genetic Algorithm and Its Advances in Embracing Memetics -- Constrained Multi-Objective Evolutionary Algorithm -- Genetic Programming for Classification and Feature Selection -- Genetic Programming for Job Shop Scheduling -- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems, Engineering, Computational intelligence, Artificial intelligence, Bansal, Jagdish Chand HerausgeberIn edt, Singh, Pramod Kumar HerausgeberIn edt, Pal, Nikhil R. HerausgeberIn (DE-627)1254724710 (DE-576)184724716 edt, 9783319913391, Erscheint auch als Druck-Ausgabe 978-3-319-91339-1, Erscheint auch als Druck-Ausgabe:, http://dx.doi.org/10.1007/978-3-319-91341-4 Verlag Volltext, https://doi.org/10.1007/978-3-319-91341-4 X:SPRINGER Resolving-System lizenzpflichtig, https://doi.org/10.1007/978-3-319-91341-4 DE-Ch1, DE-Ch1 epn:336655052X 2019-01-28T17:26:26Z, DE-105 epn:3366550619 2019-01-28T17:26:26Z, https://doi.org/10.1007/978-3-319-91341-4 DE-Zwi2, DE-Zwi2 epn:3366550716 2019-01-28T17:26:26Z, https://doi.org/10.1007/978-3-319-91341-4 HTWK-Zugang DE-L189, DE-L189 epn:3366550902 2019-01-28T17:26:26Z |
| spellingShingle | Evolutionary and Swarm Intelligence Algorithms, This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike, Swarm and Evolutionary Computation -- Particle Swarm Optimization -- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization -- Spider Monkey Optimization Algorithm -- Genetic Algorithm and Its Advances in Embracing Memetics -- Constrained Multi-Objective Evolutionary Algorithm -- Genetic Programming for Classification and Feature Selection -- Genetic Programming for Job Shop Scheduling -- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems, Engineering, Computational intelligence, Artificial intelligence |
| swb_id_str | 507095537 |
| title | Evolutionary and Swarm Intelligence Algorithms |
| title_auth | Evolutionary and Swarm Intelligence Algorithms |
| title_full | Evolutionary and Swarm Intelligence Algorithms edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal |
| title_fullStr | Evolutionary and Swarm Intelligence Algorithms edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal |
| title_full_unstemmed | Evolutionary and Swarm Intelligence Algorithms edited by Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal |
| title_short | Evolutionary and Swarm Intelligence Algorithms |
| title_sort | evolutionary and swarm intelligence algorithms |
| title_unstemmed | Evolutionary and Swarm Intelligence Algorithms |
| topic | Engineering, Computational intelligence, Artificial intelligence |
| topic_facet | Engineering, Computational intelligence, Artificial intelligence |
| url | http://dx.doi.org/10.1007/978-3-319-91341-4, https://doi.org/10.1007/978-3-319-91341-4 |
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