Title
Massively Parallel Evolutionary Computation on GPGPUs (Natural Computing Series),Used
Sold by Ergodebooks, an authorized reseller.
Returns accepted within 30 days | support@ergodebooks.com
Shipping Information
- Free Standard Shipping — United States only
- Processing Time: 1–3 business days
- Estimated Delivery: 3–5 business days after dispatch
- Double-boxed, fully insured & discreetly packaged
- Tracking number sent via email once dispatched
- Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund
Returns accepted within 30 days of delivery.
Damaged or Defective Item
Free return shipping + replacement or full refund
Wrong Item Received
Free return shipping + replacement or full refund
Change of Mind
Return shipping at customer's expense · 25% restocking fee applies
Payment Option
Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with highspeed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in generalpurpose computing on graphics processing units (GPGPU) have opened up this possibility for parallel EAs, and this is the first book dedicated to this exciting development.The three chapters of Part I are tutorials, representing a comprehensive introduction to the approach, explaining the characteristics of the hardware used, and presenting a representative project to develop a platform for automatic parallelization of evolutionary computing (EC) on GPGPUs. The 10 chapters in Part II focus on how to consider key EC approaches in the light of this advanced computational technique, in particular addressing generic local search, tabu search, genetic algorithms, differential evolution, swarm optimization, ant colony optimization, systolic genetic search, genetic programming, and multiobjective optimization. The 6 chapters in Part III present successful results from realworld problems in data mining, bioinformatics, drug discovery, crystallography, artificial chemistries, and sudoku.Although the parallelism of EAs is suited to the singleinstruction multipledata (SIMD)based GPU, there are many issues to be resolved in design and implementation, and a key feature of the contributions is the practical engineering advice offered. This book will be of value to researchers, practitioners,and graduate students in the areas of evolutionary computation and scientific computing.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.