Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TEVC.2015.2424410 | Evolutionary Computation, IEEE Transactions |
Keywords | Field | DocType |
even parity problem,evolutionary computation,gene expression programming,genetic programming,symbolic regression problem | Gene expression programming,Mathematical optimization,Constructive,Differential evolution,Artificial intelligence,Symbolic regression,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
PP | 99 | 1089-778X |
Citations | PageRank | References |
20 | 0.96 | 33 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-hui Zhong | 1 | 380 | 33.00 |
| Yew-Soon Ong | 2 | 4205 | 224.11 |
| Wentong Cai | 3 | 1928 | 197.81 |