Abstract
MEP is a variant of genetic program applied to solve the symbol regression and classification problems. It can encode multiple solutions of a problem in a single chromosome. However, when the ratio of genes reuse is low, it may not get a high accuracy result within limited iterations and may fall into the trap of local optimum. Therefore, we proposed a novel genetic evolutionary algorithm named MREP (multi-reference expression programming). The MREP chromosome is encoded in a two-dimensional structure and each gene in one chromosome can refer other sub-layer’s gene randomly. The main contribution can be described as follows: Firstly, a novel chromosome encoding scheme is proposed based on a two-dimensional structure. Secondly, two different cross-layer reference strategies are designed to enhance the code reuse of genes located at different layers in one chromosome. Two groups experiments were conducted on eight symbol regression functions. The statistical results reveal that the MREP performs better than the compared algorithms and can solve the symbol regression functions problem efficiently.
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Acknowledgement
This work was supported by National Natural Science Foundation of China under Grant No. 61572230, No. 61173078, No. 61573166 and Shandong Provincial Natural Science Foundation under Grant ZR2015JL025. The authors would like to thank the anonymous reviewers for providing comments to help us improve the contents of this paper.
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Zhang, Q., Meng, X., Yang, B., Liu, W. (2016). MREP: Multi-Reference Expression Programming. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_3
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DOI: https://doi.org/10.1007/978-3-319-42294-7_3
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