An analysis of constructive crossover and selection pressure in genetic programming

A common problem in genetic programming search algorithms is destructive crossover in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. This paper reports on experiments demonstrating that premature convergence may happen more often when using these techniques in combination with standard parent selection. It shows that modifying the selection pressure in the parent selection process is necessary to obtain a significant performance improvement. Categories and Subject Descriptors I.2 [Artificial Intelligence]: Problem Solving, Control Methods, and Search General Terms Performance Keywords Genetic Programming, Crossover, Stochastic Elements, Selection Pressure

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Huayang Xie, Mengjie Zhang, Peter Andreae
• Keywords: GECCO 2007, Genetic Programming, Parent Selection, Selection Pressure