Name
Abstract | ||
|---|---|---|
Evolutionary computation methods have been used to solve several optimization and learning problems. This paper describes an application of evolutionary computation methods to constants optimization in genetic programming. A general evolution strategy technique is proposed for approximating the optimal constants in a computer program representing the solution of a symbolic regression problem. The new algorithm has been compared with a recent linear genetic programming approach based on straight-line programs. The experimental results show that the proposed algorithm improves such technique. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICTAI.2009.35 | Newark, NJ |
Keywords | Field | DocType |
genetic algorithms,regression analysis,computer program,constants optimization,evolutionary computation methods,learning problems,linear genetic programming approach,symbolic regression problem,Evolution Strategy,Straight-line Program,Symbolic Regression | Computer science,Genetic programming,Evolution strategy,Artificial intelligence,Linear genetic programming,Evolutionary programming,Interactive evolutionary computation,Mathematical optimization,Meta-optimization,Evolutionary computation,Algorithm,Genetic representation,Machine learning | Conference |
ISSN | ISBN | Citations |
1082-3409 E-ISBN : 978-0-7695-3920-1 | 978-0-7695-3920-1 | 3 |
PageRank | References | Authors |
0.48 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| César L. Alonso | 1 | 27 | 4.69 |
| Josè L. Montaña | 2 | 82 | 15.50 |
| CRUZ ENRIQUE BORGES | 3 | 13 | 3.06 |