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GECCO
2007
Springer

An analysis of constructive crossover and selection pressure in genetic programming

9 years 7 months ago
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
Huayang Xie, Mengjie Zhang, Peter Andreae
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Huayang Xie, Mengjie Zhang, Peter Andreae
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