Gene Expression Programming: A Survey

被引:119
作者
Zhong, Jinghui [1 ]
Feng, Liang [2 ]
Ong, Yew-Soon [3 ]
机构
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China
[2] Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China
[3] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore
基金
中国国家自然科学基金;
关键词
EVOLUTIONARY ALGORITHMS; CLASSIFICATION RULES; OPTIMIZATION; PREDICTION; PERFORMANCE; COMPUTATION; PARAMETERS; SELECTION; VELOCITY; STRATEGY;
D O I
10.1109/MCI.2017.2708618
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements and developments. A number of enhanced GEPs have been proposed to date and the real world applications that use them are also multiplying fast. In view of the steadfast growth of GEP and its importance to both the academia and industry, here a review on GEP is considered. In particular, this paper presents a comprehensive review on the recent progress of GEP. The state-of-the-art approaches of GEP, with enhanced designs from six aspects, i.e., encoding design, evolutionary mechanism design, adaptation design, cooperative coevolutionary design, constant creation design, and parallel design, are presented. The theoretical studies and intriguing representative applications of GEP are given. Finally, a discussion of potential future research directions of GEP is also provided.
引用
收藏
页码:54 / 72
页数:19
相关论文
共 144 条
[1]  
[Anonymous], P 6 ANN ACM C GEN EV
[2]  
[Anonymous], COMPUT AIDED CIVIL I
[3]  
[Anonymous], P INT C NAT COMP AUG
[4]  
[Anonymous], 2014, THESIS
[5]  
[Anonymous], 2008, P 10 ANN C GENETIC E, DOI DOI 10.1145/1389095.1389332
[6]  
[Anonymous], P IEEE INT C WIR COM
[7]  
[Anonymous], 2016, IEEE T CYBERNETICS
[8]  
[Anonymous], P INSTR MEAS TECHN C
[9]  
[Anonymous], INT J MATH MODELS ME
[10]  
[Anonymous], 2014, THESIS VICTORIA U WE