JACIII Vol.13 No.3 pp. 237-244
doi: 10.20965/jaciii.2009.p0237


A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification

Naoki Mori*, Bob McKay*2, Nguyen Xuan Hoai*3,
Daryl Essam*4, and Saori Takeuchi*5

*Osaka Prefecture University, Osaka, Japan

*2Seoul National University, Seoul, Korea

*3Seoul National University, Seoul, Korea

*4University of New South Wales ADFA, Canberra, Australia

*5Mitsubishi Electric Corporation, Hyogo, Japan

November 25, 2008
February 18, 2009
May 20, 2009
genetic programming, subtree entropy, equivalent decision simplification, diversity
Symbolic Regression is one of the most important applications of Genetic Programming, but suffers from one of the key issues in Genetic Programming, bloat. For a variety of reasons, reliable techniques to remove bloat are highly desirable. This paper introduces a novel approach of removing bloat, Equivalent Decision Simplification, in which subtrees are evaluated over the set of regression points. The effectiveness of the proposed method is confirmed by computer simulation taking simple Symbolic Regression problems as examples.
Cite this article as:
N. Mori, B. McKay, N. Hoai, D. Essam, and S. Takeuchi, “A New Method for Simplifying Algebraic Expressions in Genetic Programming Called Equivalent Decision Simplification,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.3, pp. 237-244, 2009.
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