Matching Subtrees in Genetic Programming Crossover Operator

0491993 - ÚI 2019 RIV US eng C - Conference Paper (international conference)
Šlapák, M. - Neruda, Roman
Matching Subtrees in Genetic Programming Crossover Operator.
ICNC-FSKD 2017. Proceedings of International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Piscataway: IEEE, 2017, s. 208-213. ISBN 978-1-5386-2165-3.
[ICNC-FSKD 2017. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery /13./. Guilin (CN), 29.07.2017-31.07.2017]
R&D Projects: GA ČR GA15-19877S
Grant - others:ČVUT(CZ) SGS17/210/OHK3/3T/18
Institutional support: RVO:67985807
Keywords : Semantics * Genetic programming * Benchmark testing * Encoding * Standards * Computer science * Electronic mail
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

In this paper we study techniques that should reduce the destructive impact of crossover in genetic programming. The quality of crossover offsprings 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 utilizes 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.
Permanent Link: http://hdl.handle.net/11104/0285591