- author = "Jinghui Zhong and Yew-Soon Ong and Wentong Cai",
- title = "Self-Learning Gene Expression Programming",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2016",
- volume = "20",
- number = "1",
- pages = "65--78",
- month = feb,
- keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Even Parity Problem, Evolutionary Computation, Symbolic Regression Problem, GEP-ADF, TreeDE",
- ISSN = "1089-778X",
-
DOI = "
10.1109/TEVC.2015.2424410",
- size = "16 pages",
- abstract = "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.",
-
notes = "School of Computer Engineering, Nanyang Technological
University, Singapore.
Also known as \cite{7089238}",
