Published 1993
by IOS Press in Amsterdam, Washington, DC .
Written in
Edition Notes
Includes bibliographical references.
| Statement | edited by Joachim Stender. |
| Series | Frontiers in artificial intelligence and applications, |
| Contributions | Stender, J. |
| Classifications | |
|---|---|
| LC Classifications | QA402.5 .P378 1993 |
| The Physical Object | |
| Pagination | 217 p. : |
| Number of Pages | 217 |
| ID Numbers | |
| Open Library | OL1744352M |
| ISBN 10 | 9051990871 |
| LC Control Number | 92053268 |
| OCLC/WorldCa | 28204465 |
As genetic algorithms (GAs) become increasingly popular, they are applied to difficult problems that may require considerable computations. Efficient and Accurate Parallel Genetic Algorithms is Read more. Yao X Global Optimisation by Evolutionary Algorithms Proceedings of the 2nd AIZU International Symposium on Parallel Algorithms / Architecture Synthesis Vigo D and Maniezzo V () A Genetic/Tabu Thresholding Hybrid Algorithm for the ProcessAllocation Problem, Journal of Heuristics, , (), Online publication date: 1-Dec Parallel Genetic Algorithms Theory and Real World Applications. Roger Lee & Naohiro Ishii. $; $; Publisher Description. The purpose of the 10th Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD ) to be held on May 27 – 29, in Daegu, Korea is to bring together. Parallel Genetic Algorithms: Theory and Real World Applications. Abstract. This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times.
This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Genetic Algorithms in Java Basics Book is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. A Survey of Parallel Genetic Algorithms Erick Cantú-Paz Department of Computer Science and Illinois Genetic Algorithms Laboratory University of Illinois at Urbana-Champaign cantupaz@ ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to solve problems in many different disciplines. Here we use parallel Genetic Algorithms (GA), as implemented in our MGAC software, directly coupled with DFT energy calculations to show that the global search of CuSi(n) cluster structures does.
Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing and logistic fields. It helps to find better solutions for complex and difficult cases, which are hard to be solved by using strict optimization methods. Accelerating parallel GAs with GPU computing have received significant attention from both practitioners and researchers, ever since the. Additional information on genetic programming can be found in the book Genetic Programming III: Darwinian Invention and Problem Solving and its accompanying for citations and links to numerous other authored books, edited collections of papers, conference proceedings, and individual published papers concerning genetic programming. "This book nicely combines many papers on a general topic of timeliness and importance." (Journal of the Operational Research Society, ) " a good overview of recent metaheuristic techniques, and can be used as a starting point for developing new parallel version of the methods."Computing This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Rating: (not yet rated) 0 with reviews - Be the first.
