Ant-based metaheuristics struggle to solve the Cartesian Genetic Programming learning task

  • Ant-based metaheuristics have successfully been applied to a variety of different graph-based problems. However, for Cartesian Genetic Programming (CGP) only the impact of Max-Min Ant Systems has been tested. In this work, we try to fill this gap by applying four different popular ant-based metaheuristics as the optimizer (and therefore training algorithm) of CGP. The idea of combining CGP with ant-based metaheuristics is not novel but older works’ experimental design may not meet today’s standard. To compare these metaheuristics to the Evolution Strategies (ESs) commonly used in CGP, we benchmark against a standard CGP variant that uses a simplistic (1 + 4)-ES, mutation, and no crossover. Additionally, we include (μ + λ)-ES and (μ, λ)-ES in our experiments. We analyse the performance on datasets from the symbolic regression, regression, and classification domains. By tuning and evaluating various configurations, we can not affirm a significant improvement by usingAnt-based metaheuristics have successfully been applied to a variety of different graph-based problems. However, for Cartesian Genetic Programming (CGP) only the impact of Max-Min Ant Systems has been tested. In this work, we try to fill this gap by applying four different popular ant-based metaheuristics as the optimizer (and therefore training algorithm) of CGP. The idea of combining CGP with ant-based metaheuristics is not novel but older works’ experimental design may not meet today’s standard. To compare these metaheuristics to the Evolution Strategies (ESs) commonly used in CGP, we benchmark against a standard CGP variant that uses a simplistic (1 + 4)-ES, mutation, and no crossover. Additionally, we include (μ + λ)-ES and (μ, λ)-ES in our experiments. We analyse the performance on datasets from the symbolic regression, regression, and classification domains. By tuning and evaluating various configurations, we can not affirm a significant improvement by using ant-based methods with CGP as we encounter premature convergence—even with those ant-based metaheuristics that were originally proposed to overcome such problems. Despite our results being of negative nature, this work still gives important and interesting insights into the training of CGP models. The key contributions of our work are thus a more thorough benchmarking of these optimizers than has been done before. This should clear up doubts about the capabilities of ant-based metaheuristics in CGP. Furthermore, we include a roadmap on how they can be addressed to solve this complex optimization problem from the model building domain of machine learning.show moreshow less

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Metadaten
Author:Julian TrautweinORCiD, Michael HeiderORCiDGND, Henning CuiORCiDGND, Jörg HähnerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1215983
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/121598
ISBN:978-3-031-89991-1OPAC
ISSN:0302-9743OPAC
Parent Title (English):Genetic Programming: 28th European Conference, EuroGP 2025, held as part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, proceedings
Publisher:Springer
Place of publication:Cham
Editor:Bing Xue, Luca Manzoni, Illya Bakurov
Type:Conference Proceeding
Language:English
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2025/04/28
First Page:139
Last Page:155
Series:Lecture Notes in Computer Science ; 15609
DOI:https://doi.org/10.1007/978-3-031-89991-1_9
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht