A Comparison of Structural Complexity Metrics for Explainable Genetic Programming [Poster]

Karina Brotto Rebuli, Mario Giacobini, Sara Silva, Leonardo Vanneschi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

56 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationGECCO '23 Companion
Subtitle of host publicationProceedings of the Companion Conference on Genetic and Evolutionary Computation
EditorsSara Silva, Luís Paquete
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Print)979-8-4007-0120-7
Publication statusPublished - 24 Jul 2023
EventThe Genetic and Evolutionary Computation Conference (GECCO 2023) - Lisbon, Portugal
Duration: 15 Jul 202319 Jul 2023
Conference number: 2023


ConferenceThe Genetic and Evolutionary Computation Conference (GECCO 2023)
Abbreviated titleGECCO 2023
Internet address


  • explainable AI
  • interpretable models
  • complexity metrics


Dive into the research topics of 'A Comparison of Structural Complexity Metrics for Explainable Genetic Programming [Poster]'. Together they form a unique fingerprint.

Cite this