Matching subtrees in genetic programming crossover operator

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Slapak:2017:ICNC-FSKD,

• author = "Martin Slapak and Roman Neruda",
• booktitle = "2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)",
• title = "Matching subtrees in genetic programming crossover operator",
• year = "2017",
• pages = "208--213",
• month = jul,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/FSKD.2017.8393091",
• abstract = "we study techniques that should reduce the destructive impact of crossover in genetic programming. The quality of crossover children is often lower than ancestors due to the fact that a small change in individual's genotype tree structure has a great impact to its phenotype. Therefore we propose and test several methods for matching subtrees to find the best possible cutting point for crossover of trees. Our approach uses the adaptive probability of operators with the intent to reinforce the well-performing operators. A relation to the semantic genetic programming approach is also investigated. The experimental results show that the average arity based technique performs best from the proposed methods.",
• notes = "Also known as \cite{8393091}",

}

Genetic Programming entries for Martin Slapak Roman Neruda

Citations