Intelligent decision making through bio-inspired optimization:
Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with...
Gespeichert in:
| Weitere Verfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey PA, USA
IGI Global
[2024]
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| Schriftenreihe: | Advances in computational intelligence and robotics (ACIR) book series
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| Schlagworte: | |
| Online-Zugang: | DE-91 DE-898 DE-1050 Volltext |
| Zusammenfassung: | Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving.In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape. |
| Beschreibung: | 1 Online-Ressource (xvi, 275 Seiten) Illustrationen |
| ISBN: | 9798369320747 |
| DOI: | 10.4018/979-8-3693-2073-0 |
Internformat
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| 520 | |a Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving.In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape. | ||
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Datensatz im Suchindex
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| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-sort | 3519.3 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.4018/979-8-3693-2073-0 |
| format | Electronic eBook |
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| id | DE-604.BV049735920 |
| illustrated | Illustrated |
| indexdate | 2024-12-17T19:01:36Z |
| institution | BVB |
| isbn | 9798369320747 |
| language | English |
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| physical | 1 Online-Ressource (xvi, 275 Seiten) Illustrationen |
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| publisher | IGI Global |
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| series2 | Advances in computational intelligence and robotics (ACIR) book series |
| spelling | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan Hershey PA, USA IGI Global [2024] 1 Online-Ressource (xvi, 275 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Advances in computational intelligence and robotics (ACIR) book series Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving.In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape. Algorithms Artificial intelligence Decision making Mathematical optimization Jaganathan, Ramkumar 1986- edt Mehta, Shilpa edt Krishan, Ram edt Erscheint auch als Druck-Ausgabe 979-8-3693-2073-0 Erscheint auch als Druck-Ausgabe, Paperback 979-8-3693-4692-1 https://doi.org/10.4018/979-8-3693-2073-0 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Intelligent decision making through bio-inspired optimization Algorithms Artificial intelligence Decision making Mathematical optimization |
| title | Intelligent decision making through bio-inspired optimization |
| title_auth | Intelligent decision making through bio-inspired optimization |
| title_exact_search | Intelligent decision making through bio-inspired optimization |
| title_full | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan |
| title_fullStr | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan |
| title_full_unstemmed | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan |
| title_short | Intelligent decision making through bio-inspired optimization |
| title_sort | intelligent decision making through bio inspired optimization |
| topic | Algorithms Artificial intelligence Decision making Mathematical optimization |
| topic_facet | Algorithms Artificial intelligence Decision making Mathematical optimization |
| url | https://doi.org/10.4018/979-8-3693-2073-0 |
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