Matching Subtrees in Genetic Programming Crossover Operator
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00491993" target="_blank" >RIV/67985807:_____/17:00491993 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/FSKD.2017.8393091" target="_blank" >http://dx.doi.org/10.1109/FSKD.2017.8393091</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/FSKD.2017.8393091" target="_blank" >10.1109/FSKD.2017.8393091</a>
Alternative languages
Result language
angličtina
Original language name
Matching Subtrees in Genetic Programming Crossover Operator
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICNC-FSKD 2017. Proceedings of International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
ISBN
978-1-5386-2165-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
208-213
Publisher name
IEEE
Place of publication
Piscataway
Event location
Guilin
Event date
Jul 29, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000437355300035