A Comparison of Structural Complexity Metrics for Explainable Genetic Programming-Reference-Cited by-同舟云学术

A Comparison of Structural Complexity Metrics for Explainable Genetic Programming

Published:2023-07-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Companion Conference on Genetic and Evolutionary Computation
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Author:

Brotto Rebuli Karina¹^ORCID,Giacobini Mario¹^ORCID,Silva Sara²^ORCID,Vanneschi Leonardo³^ORCID

Affiliation:

1. University of Torino, Grugliasco, Italy

2. Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal

3. Universidade Nova de Lisboa, Lisbon, Portugal

Funder

FCT, Portugal

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583133.3590595

Reference19 articles.

1. Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

2. It Is Time for New Perspectives on How to Fight Bloat in GP

3. Maarten Keijzer and James Foster . 2007. Crossover Bias in Genetic Programming . In Genetic Programming, Marc Ebner, Michael O'Neill, Aniko Ekárt, Leonardo Vanneschi, and Anna Isabel Esparcia-Alcázar (Eds.) . Springer Berlin Heidelberg , Berlin, Heidelberg , 33--44. Maarten Keijzer and James Foster. 2007. Crossover Bias in Genetic Programming. In Genetic Programming, Marc Ebner, Michael O'Neill, Aniko Ekárt, Leonardo Vanneschi, and Anna Isabel Esparcia-Alcázar (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 33--44.

4. Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming

5. John R. Koza . 1992. Genetic programming: On the programming of computers by means of natural selection . MIT Press . John R. Koza. 1992. Genetic programming: On the programming of computers by means of natural selection. MIT Press.

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