Presentation 2000/5/18
Improving Performance of GP by Adaptive Terminal Selection
OK Sooyol, Miyashita Kazuo, Nishihara Seiichi,
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Abstract(in English) 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 adaptivemutation based on terminal weighting mechanism for eliminating irrelevant terminals from the redundant terminal set. We show empirically that the proposed method is effective for find relevant terminals and improving performance of GP in the experiments on symbolic regression problems.
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Keyword(in English) Genetic Programming / Feature Selection / Adaptive Mutation
Paper # AI2000-7
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Committee AI
Conference Date 2000/5/18(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Improving Performance of GP by Adaptive Terminal Selection
Sub Title (in English)
Keyword(1) Genetic Programming
Keyword(2) Feature Selection
Keyword(3) Adaptive Mutation
1st Author's Name OK Sooyol
1st Author's Affiliation Doctoral Program in Engineering University of Tsukuba()
2nd Author's Name Miyashita Kazuo
2nd Author's Affiliation Doctoral Program in Engineering University of Tsukuba
3rd Author's Name Nishihara Seiichi
3rd Author's Affiliation Doctoral Program in Engineering University of Tsukuba
Date 2000/5/18
Paper # AI2000-7
Volume (vol) vol.100
Number (no) 88
Page pp.pp.-
#Pages 8
Date of Issue