Agent-Based Evolutionary Search: Adaptation, Learning, and Optimization, cartea 5
Editat de Ruhul A. Sarker, Tapabrata Rayen Limba Engleză Hardback – 9 iun 2010
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 616.15 lei 6-8 săpt. | |
| Springer Berlin, Heidelberg – 5 sep 2012 | 616.15 lei 6-8 săpt. | |
| Hardback (1) | 620.55 lei 6-8 săpt. | |
| Springer Berlin, Heidelberg – 9 iun 2010 | 620.55 lei 6-8 săpt. |
Din seria Adaptation, Learning, and Optimization
- 15%
Preț: 612.68 lei - 15%
Preț: 609.85 lei - 20%
Preț: 945.63 lei - 20%
Preț: 623.39 lei - 20%
Preț: 2329.16 lei - 20%
Preț: 945.95 lei - 20%
Preț: 947.07 lei - 20%
Preț: 628.15 lei - 20%
Preț: 623.52 lei - 15%
Preț: 615.66 lei - 20%
Preț: 624.33 lei - 20%
Preț: 626.25 lei - 18%
Preț: 1765.85 lei - 20%
Preț: 621.64 lei - 20%
Preț: 946.13 lei - 20%
Preț: 681.25 lei - 20%
Preț: 621.49 lei - 20%
Preț: 948.32 lei - 20%
Preț: 613.87 lei - 20%
Preț: 945.18 lei - 20%
Preț: 952.62 lei - 15%
Preț: 613.94 lei - 15%
Preț: 616.15 lei - 20%
Preț: 612.62 lei - 20%
Preț: 1392.65 lei - 20%
Preț: 1234.45 lei - 20%
Preț: 947.07 lei
Preț: 620.55 lei
Preț vechi: 730.06 lei
-15% Nou
Puncte Express: 931
Preț estimativ în valută:
109.81€ • 128.76$ • 96.44£
109.81€ • 128.76$ • 96.44£
Carte tipărită la comandă
Livrare economică 04-18 februarie 26
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783642134241
ISBN-10: 3642134246
Pagini: 291
Ilustrații: 291 p. 48 illus. in color.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.56 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Adaptation, Learning, and Optimization
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642134246
Pagini: 291
Ilustrații: 291 p. 48 illus. in color.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.56 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Adaptation, Learning, and Optimization
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Agent Based Evolutionary Approach: An Introduction.- Multi-Agent Evolutionary Model for Global Numerical Optimization.- An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints.- Multiagent-Based Approach for Risk Analysis in Mission Capability Planning.- Agent Based Evolutionary Dynamic Optimization.- Divide and Conquer in Coevolution: A Difficult Balancing Act.- Complex Emergent Behaviour from Evolutionary Spatial Animat Agents.- An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller.- An Attempt to Stochastic Modeling of Memetic Systems.- Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm.- PSO (Particle Swarm Optimization): One Method, Many Possible Applications.- VISPLORE: Exploring Particle Swarms by Visual Inspection.
Textul de pe ultima copertă
The performance of Evolutionary Algorithms can be enhanced by integrating the concept of agents. Agents and Multi-agents can bring many interesting features which are beyond the scope of traditional evolutionary process and learning.
This book presents the state-of-the art in the theory and practice of Agent Based Evolutionary Search and aims to increase the awareness on this effective technology. This includes novel frameworks, a convergence and complexity analysis, as well as real-world applications of Agent Based Evolutionary Search, a design of multi-agent architectures and a design of agent communication and learning Strategy.
This book presents the state-of-the art in the theory and practice of Agent Based Evolutionary Search and aims to increase the awareness on this effective technology. This includes novel frameworks, a convergence and complexity analysis, as well as real-world applications of Agent Based Evolutionary Search, a design of multi-agent architectures and a design of agent communication and learning Strategy.
Caracteristici
State-of-the art in theory and practice of Agent Based Evolutionary Search Includes novel frameworks and real-world applications of Agent Based Evolutionary Search Written by leading experts in this field