| ID | 30111 |
| FullText URL | |
| Author |
Horiuchi, Tadashi
Katai, Osamu
Kaneko, Takeshi
Konishi, Tadataka
Baba, Mitsuru
|
| Abstract | The authors introduce a novel coevolutionary genetic algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in a natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm the effectiveness of the proposed method |
| Keywords | genetic algorithms
knapsack problems
learning (artificial intelligence)
search problems
|
| Note | Digital Object Identifier: 10.1109/CEC.2001.934329
Published with permission from the copyright holder. This is the institute's copy, as published in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 27-30 May 2001, Vol. 2, Pages 1213-1219. Publisher URL:http://dx.doi.org/10.1109/CEC.2001.934329 Copyright © 2001 IEEE. All rights reserved. |
| Published Date | 2001-5
|
| Publication Title |
Evolutionary Computation
|
| Volume | volume2
|
| Start Page | 1213
|
| End Page | 1219
|
| Content Type |
Journal Article
|
| language |
English
|
| Refereed |
True
|
| DOI | |
| Submission Path | industrial_engineering/34
|
