A Comparison of Structural Complexity Metrics for Explainable Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7650

@InProceedings{brotto-rebuli:2023:GECCOcomp,

• author = "Karina {Brotto Rebuli} and Mario Giacobini and Sara Silva and Leonardo Vanneschi",
• title = "A Comparison of Structural Complexity Metrics for Explainable Genetic Programming",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "539--542",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, complexity metrics, explainable AI, XAI, interpretable models: Poster",
• isbn13 = "9798400701191",
• URL = "https://novaresearch.unl.pt/en/publications/a-comparison-of-structural-complexity-metrics-for-explainable-gen",
• URL = "https://novaresearch.unl.pt/files/67865641/Comparison_Structural_Complexity_Metrics_for_Explainable_Genetic_Programming.pdf",
• DOI = "doi:10.1145/3583133.3590595",
• size = "4 pages",
• abstract = "Genetic Programming (GP) has the potential to generate intrinsically explainable models. Despite that, in practice, this potential is not fully achieved because the solutions usually grow too much during the evolution. The excessive growth together with the functional and structural complexity of the solutions increase the computational cost and the risk of overfitting. Thus, many approaches have been developed to prevent the solutions to grow excessively in GP. However, it is still an open question how these approaches can be used for improving the interpretability of the models. This article presents an empirical study of eight structural complexity metrics that have been used as evaluation criteria in multi-objective optimisation. Tree depth, size, visitation length, number of unique features, a proxy for human interpretability, number of operators, number of non-linear operators and number of consecutive nonlinear operators were tested. The results show that potentially the best approach for generating good interpretable GP models is to use the combination of more than one structural complexity metric.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Karina Brotto Rebuli Mario Giacobini Sara Silva Leonardo Vanneschi

Citations