Ant-based Metaheuristics Struggle to Solve the Cartesian Genetic Programming Learning Task

Created by W.Langdon from gp-bibliography.bib Revision:1.8229

@InProceedings{trautwein:2025:EuroGP,

• author = "Julian Trautwein and Michael Heider and Henning Cui and Joerg Haehner",
• title = "Ant-based Metaheuristics Struggle to Solve the Cartesian Genetic Programming Learning Task",
• booktitle = "European Conference on Genetic Programming, EuroGP 2025",
• year = "2025",
• editor = "Bing Xue and Luca Manzoni and Illya Bakurov",
• series = "LNCS",
• address = "Trieste",
• month = "23-25 " # apr,
• publisher = "Springer Nature",
• note = "Forthcoming",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Evolution Strategies, Evolutionary Algorithm, Ant Colony Optimization, ACO, Ant-based Meta-heuristic",
• abstract = "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 optimiser (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 ($$\mu + \lambda $$)-ES and (($$\mu,\ \lambda $$)-ES in our experiments. We analyse the performance on the datasets from the symbolic regression, regression and classification domains. By tuning and evaluating various configurations, we cannot 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 this more thorough benchmarking of these optimisers that 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 optimisation problem from the model building domain of machine learning.",
• notes = "also known as \cite{paper_64_cartesian_genetic_programming_learning_task}

  Part of \cite{Xue:2025:GP} EuroGP'2025 held in conjunction with EvoCOP2025, EvoMusArt2025 and EvoApplications2025",

}

Genetic Programming entries for Julian Trautwein Michael Heider Henning Cui Joerg Haehner

Citations