Foundations of Genetic Algorithms 2001 (FOGA 6)
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Programming
| Author: | Worth Martin | ISBN: | 9780080506876 |
| Publisher: | Elsevier Science | Publication: | July 18, 2001 |
| Imprint: | Morgan Kaufmann | Language: | English |
| Author: | Worth Martin |
| ISBN: | 9780080506876 |
| Publisher: | Elsevier Science |
| Publication: | July 18, 2001 |
| Imprint: | Morgan Kaufmann |
| Language: | English |
Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems.
Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones.
- Includes research from academia, government laboratories, and industry
- Contains high calibre papers which have been extensively reviewed
- Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field
- Ideal for researchers in machine learning, specifically those involved with evolutionary computation
Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems.
Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones.
- Includes research from academia, government laboratories, and industry
- Contains high calibre papers which have been extensively reviewed
- Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field
- Ideal for researchers in machine learning, specifically those involved with evolutionary computation
