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Semantic approximation for reducing code bloat in Genetic Programming

Nguyen, Q.U. and Chu, T.H. (2020) Semantic approximation for reducing code bloat in Genetic Programming. Swarm and Evolutionary Computation, 58: 100729. ISSN 22106502

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Abstract

Code bloat is a phenomenon in Genetic Programming (GP) characterized by the increase in individual size during the evolutionary process without a corresponding improvement in fitness. Bloat negatively affects GP performance, since large individuals are more time consuming to evaluate and harder to interpret. In this paper, we propose two approaches for reducing GP code bloat based on a semantic approximation technique. The first approach replaces a random subtree in an individual by a smaller tree of approximate semantics. The second approach replaces a random subtree by a smaller tree that is semantically approximate to the desired semantics. We evaluated the proposed methods on a large number of regression problems. The experimental results showed that our methods help to significantly reduce code bloat and improve the performance of GP compared to standard GP and some recent bloat control methods in GP. Furthermore, the performance of the proposed approaches is competitive with the best machine learning technique among the four tested machine learning algorithms. © 2020 Elsevier B.V.

Item Type: Article
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1016/j.swevo.2020.100729
Uncontrolled Keywords: Forestry; Genetic algorithms; Learning algorithms; Machine learning; Semantics; Code bloats; Control methods; Evolutionary process; Individual size; Machine learning techniques; Regression problem; Semantic approximation; Sub trees; Genetic programming
Additional Information: Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8888

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