| Vorliegende Sprache |
eng |
| ISBN |
978-981-13-9262-7 |
| Name |
Dey, Nilanjan ¬[HerausgeberIn]¬ |
| Ashour, Amira S. ¬[HerausgeberIn]¬ |
| Name ANZEIGE DER KETTE |
Ashour, Amira S. ¬[HerausgeberIn]¬ |
| Name |
Bhattacharyya, Siddhartha ¬[HerausgeberIn]¬ |
| T I T E L |
Applied Nature-Inspired Computing: Algorithms and Case Studies |
| Auflage |
1st ed. 2020 |
| Verlagsort |
Singapore |
| Verlag |
Springer |
| Erscheinungsjahr |
2020 |
| 2020 |
| Umfang |
1 Online-Ressource (XII, 275 p. 134 illus., 89 illus. in color) |
| Reihe |
Springer Tracts in Nature-Inspired Computing |
| Titelhinweis |
Erscheint auch als (Druck-Ausgabe)ISBN: 978-981-13-9262-7 |
| ISBN |
ISBN 978-981-13-9263-4 |
| Klassifikation |
COM004000 |
| UYQ |
| UYQ |
| TEC009000 |
| 006.3 |
| Q342 |
| Kurzbeschreibung |
Chapter 1. Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation -- Chapter 2. Detection of Breast Cancer using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly algorithm and Optimum Path Forest Classification -- Chapter 3. Recommending Healthy Personalized Daily Menus – A Cuckoo Search based Hyper-Heuristic Approach -- Chapter 4. A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network -- Chapter 5. An Application of Binary Grey Wolf Optimizer (BGWO) variants for Unit Commitment Problem -- Chapter 6. Sensorineural hearing loss identification via discrete wavelet packet entropy and cat swarm optimization -- Chapter 7. Chaotic Variants of Grasshopper Optimisation Algorithm and their application to Protein Structure Prediction -- Chapter 8. Examination of Retinal Anatomical Structures – A Study with Spider Monkey Optimization Algorithm -- Chapter 9. Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power System: A Comparative Study -- Chapter 10. Parallel-series System Optimization by Weighting Sum Methods and Nature-inspired Computing -- Chapter 11. Development of Artificial Neural Networks trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends |
| 2. Kurzbeschreibung |
This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management |
| SWB-Titel-Idn |
1676317198 |
| Signatur |
Springer E-Book |
| Bemerkungen |
Elektronischer Volltext - Campuslizenz |
| Elektronische Adresse |
$uhttps://doi.org/10.1007/978-981-13-9263-4 |
| Internetseite / Link |
Resolving-System |
