Improving Performance of GP by Adaptive Terminal Selection (2000) [1 citations — 0 self]
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author = {Sooyol Ok and Kazuo Miyashita and Seiichi Nishihara},
title = {Improving Performance of GP by Adaptive Terminal Selection},
year = {2000}
}
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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 su ers substantial loss of its eciency. 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.
Citations
| 1921 | Genetic Programming I : On the Programming of Computers by Means of Natural Selection – Koza - 1992 |
| 75 | A probabilistic approach to feature selection - a filter solution – LIU, SETIONO - 1996 |
| 6 | Sahami “Toward Optimal Feature Selection – Koller - 1996 |
| 5 | lil-gp 1.0 User’s Manual – Zongker, Punch - 1995 |
| 1 | and Pat Langey.: Selection of relevant features and examples in machine learning – Blum - 1997 |
