Abstract
The effects of various genetic operators and parent selection algorithms on the performance of a genetic programming system on different problems have been well studied. In this paper, we analyze how different selection algorithms influence modularity in the population of evolving programs. In particular, we observe how the number of individuals with some form of modular structure, i.e., the presence of code blocks executed multiple times, changes over generations for various selection algorithms.
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Acknowledgements
We would like to thank Michael Garcia and other members of Hampshire College Institute for Computational Intelligence for their valuable inputs.
This material is based upon work supported by the National Science Foundation under Grant No. 1617087. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.
This work was performed in part using high performance computing equipment obtained under a grant from the Collaborative R&D Fund managed by the Massachusetts Technology Collaborative.
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Saini, A.K., Spector, L. (2020). Effect of Parent Selection Methods on Modularity. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_12
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DOI: https://doi.org/10.1007/978-3-030-44094-7_12
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