An Extension of Gene Expression Programming with Hybrid Selection

Liu, Julie Yu‐Chih; Chen, Jeng-Her Alex; Chiu, Chiang-Tien; Hsieh, Juo-Chiang

doi:10.1007/978-3-319-04573-3_79

Cited by 3 publications

(1 citation statement)

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“…Existing works mainly focus on hybridizing new operations or adaptive parameter controlling strategies with GEP to improve the search efficiency. For example, Liu et al [27] proposed an extended GEP which adaptively replaces worse individuals with randomly generated individuals, so as to increase the population diversity. A hybrid selection method was also adopted to improve the search efficiency.…”

Section: Other Related Gep Variantsmentioning

confidence: 99%

Self-Learning Gene Expression Programming

Zhong

Ong

Cai

2016

IEEE Trans. Evol. Computat.

108

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In this paper, a novel self-learning gene expression programming (GEP) methodology named SL-GEP is proposed to improve the search accuracy and efficiency of GEP. In contrast to the existing GEP variants, the proposed SL-GEP features a novel chromosome representation where each chromosome is embedded with subfunctions that can be deployed to construct the final solution. As part of the chromosome, the subfunctions are self-learned or self-evolved by the proposed algorithm during the evolutionary search. By encompassing subfunctions or any partial solution as input arguments of another subfunction, the proposed SL-GEP facilitates the formation of sophisticated, higher-order, and constructive subfunctions that improve the accuracy and efficiency of the search. Further, a novel search mechanism based on differential evolution is proposed for the evolution of chromosomes in the SL-GEP. The proposed SL-GEP is simple, generic and has much fewer control parameters than the traditional GEP variants. The proposed SL-GEP is validated on 15 symbolic regression problems and six even parity problems. Experimental results show that the proposed SL-GEP offers enhanced performances over several state-of-the-art algorithms in terms of accuracy and search efficiency.

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Section: Other Related Gep Variantsmentioning

confidence: 99%