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Semantics based substituting technique for reducing code bloat in genetic programming

Chu, T.H. and Nguyen, Q.U. and Cao, V.L. (2018) Semantics based substituting technique for reducing code bloat in genetic programming. In: 9th International Symposium on Information and Communication Technology, SoICT 2018, 6 December 2018 through 7 December 2018.

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Abstract

Genetic Programming (GP) is a technique that allows computer programs encoded as a set of tree structures to be evolved using an evolutionary algorithm. In GP, code bloat is a common phenomenon characterized by the size of individuals gradually increasing during the evolution. This phenomenon has a negative impact on GP performance in solving problems. In order to address this problem, we have recently introduced a code bloat control method based on semantics: Substituting a subtree with an Approximate Terminal (SAT-GP). In this paper, we propose an extension of SAT-GP, namely Substituting a subtree with an Approximate Subprogram (SAS-GP). We tested this method with different GP parameter settings on a real-world time series forecasting problem. The experimental results demonstrate the benefit of the proposed method in reducing the code bloat phenomenon and improving GP performance. Particularly, SAS-GP often achieves the best performance compared to other tested GP systems using four popular performance metrics in GP. © 2018 Association for Computing Machinery.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1145/3287921.3287948
Uncontrolled Keywords: Codes (symbols); Genetic algorithms; Problem solving; Semantics; Time series; Trees (mathematics); Code bloats; Code growth; Control methods; Parameter setting; Performance metrics; Real-world time series; Sub trees; Tree structures; Genetic programming
Additional Information: Conference code: 143217. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9489

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