A termék adatai:
| ISBN13: | 9780128219867 |
| ISBN10: | 0128219866 |
| Kötéstípus: | Puhakötés |
| Terjedelem: | 310 oldal |
| Méret: | 235x191 mm |
| Súly: | 570 g |
| Nyelv: | angol |
| 237 |
Témakör:
Nature-Inspired Optimization Algorithms
Kiadás sorszáma: 2
Kiadó: Academic Press
Megjelenés dátuma: 2020. szeptember 14.
Normál ár:
Kiadói listaár:
EUR 132.00
EUR 132.00
Az Ön ára:
43 576 (41 501 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 10 894 Ft)
A kedvezmény érvényes eddig: 2024. június 30.
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Hosszú leírás:
Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.
- Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
- Provides a theoretical understanding and practical implementation hints
- Presents a step-by-step introduction to each algorithm
- Includes four new chapters covering mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization algorithms, and the latest deep learning techniques, background and various applications
Tartalomjegyzék:
1. Introduction to Algorithms 2. Mathematical Foundations3. Analysis of Algorithms4. Random Walks and Optimization5. Simulated Annealing6. Genetic Algorithms7. Differential Evolution8. Particle Swarm Optimization9. Firefly Algorithms10. Cuckoo Search11. Bat Algorithms12. Flower Pollination Algorithms13. A Framework for Self-Tuning Algorithms14. How to Deal With Constraints15. Multi-Objective Optimization16. Data Mining and Deep LearningAppendix A Test Function Benchmarks for Global OptimizationAppendix B Matlab? Programs
