Nature-Inspired Optimizers [[electronic resource] ] : Theories, Literature Reviews and Applications / / edited by Seyedali Mirjalili, Jin Song Dong, Andrew Lewis
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| Titolo: |
Nature-Inspired Optimizers [[electronic resource] ] : Theories, Literature Reviews and Applications / / edited by Seyedali Mirjalili, Jin Song Dong, Andrew Lewis
|
| Pubblicazione: | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Edizione: | 1st ed. 2020. |
| Descrizione fisica: | 1 online resource (XVI, 238 p. 108 illus., 101 illus. in color.) |
| Disciplina: | 006.3823 |
| Soggetto topico: | Computational intelligence |
| Artificial intelligence | |
| Mathematical optimization | |
| Control engineering | |
| Computational Intelligence | |
| Artificial Intelligence | |
| Optimization | |
| Control and Systems Theory | |
| Persona (resp. second.): | MirjaliliSeyedali |
| Song DongJin | |
| LewisAndrew | |
| Nota di bibliografia: | Includes bibliographical references. |
| Nota di contenuto: | Preface -- Chapter 1. Introduction to Nature-inspired Algorithms -- Chapter 2. Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning.-Chapter 3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Network -- Chapter 4. Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection -- Chapter 5. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction etc. |
| Sommario/riassunto: | This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage. |
| Titolo autorizzato: | Nature-Inspired Optimizers |
| ISBN: | 3-030-12127-5 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910484569403321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
Studies in Computational Intelligence, . 1860-949X ; ; 811