GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY

被引：21
|
作者：
Oltean, Mihai [1 ]
Grosan, Crina [1 ]
Diosan, Laura [1 ]
Mihaila, Cristina [1 ]
机构：
[1] Univ Babes Bolyai, Dept Comp Sci, Fac Math & Comp Sci, Cluj Napoca 400084, Romania
来源：
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS | 2009年 / 18卷 / 02期
关键词：
Genetic programming; linear genetic programming; gene expression programming; multi expression programming; grammatical evolution; Cartesian genetic programming; stack-based genetic programmig; GRAMMATICAL EVOLUTION; MINIATURE ROBOT; NEURAL-NETWORKS; CLASSIFICATION; PERFORMANCE; CIRCUITS; DESIGN; RULES;
D O I：
10.1142/S0218213009000111
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.
引用
收藏
页码：197 / 238
页数：42
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