Title: Comparison of linear genetic programming variants for symbolic regression
Abstract: In this paper, we compare a basic linear genetic programming (LGP) algorithm against several LGP variants, proposed by us, on two sets of symbolic regression benchmarks. We evaluated the influence of methods to control bloat, investigated these techniques focused in growth of effective code, and examined an operator to consider two successful individuals as modules to be integrated into a new individual. Results suggest that methods that deal with program size, percentage of effective code, and subfunctions, can improve the quality of the final solutions.
Publication Year: 2014
Publication Date: 2014-07-11
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 4
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot