Crossover and mutation operators for grammar-guided genetic programming

作     者：Couchet, Jorge Manrique, Daniel Rios, Juan Rodriguez-Paton, Alfonso 

作者机构：Univ Politecn Madrid Fac Informat E-28660 Madrid Spain 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2007年第11卷第10期

页      面：943-955页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：grammar-guided genetic programming crossover mutation breast cancer prognosis 

摘      要：This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.