Name
Abstract | ||
|---|---|---|
Genetic Programming (GP) is an evolutionary search algorithm which searches a computer program capable of producing the desired solution for a given problem. For the purpose, it is necessary that GP system has access to a set of features that are at least a superset of the features necessary to solve the problem. However, when the feature set given to GP is redundant, GP suffers substantial loss of its efficiency. This paper presents a new approach in GP to acquire relevant terminals from a redundant set of terminals. We propose the adaptive mutation based on terminal weighting mechanism for eliminating irrelevant terminals from the redundant terminal set. We show empirically that the proposed method is effective for finding relevant terminals and improving performance of GP in the experiments on symbolic regression problems. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-44533-1_45 | PRICAI |
Keywords | Field | DocType |
search algorithm | Subset and superset,Weighting,Search algorithm,Adaptive mutation,Computer science,Genetic programming,Feature set,Artificial intelligence,Computer program,Symbolic regression,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-67925-1 | 6 | 0.52 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soo-Yol Ok | 1 | 22 | 2.66 |
| Kazuo Miyashita | 2 | 91 | 16.73 |
| Seiichi Nishihara | 3 | 71 | 14.35 |