On-the-fly simplification of genetic programming models

Javed, Noman ORCID: 0000-0001-7770-6616 and Gobet, Fernand ORCID: 0000-0002-9317-6886 (2021) On-the-fly simplification of genetic programming models. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021. Proceedings of the ACM Symposium on Applied Computing. ACM Press, New York, NY, 464 – 471. ISBN 97814503810421

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Abstract

The last decade has seen amazing performance improvements in deep learning. However, the black-box nature of this approach makes it difficult to provide explanations of the generated models. In some fields such as psychology and neuroscience, this limitation in explainability and interpretability is an important issue. Approaches such as genetic programming are well positioned to take the lead in these fields because of their inherent white box nature. Genetic programming, inspired by Darwinian theory of evolution, is a population-based search technique capable of exploring a highdimensional search space intelligently and discovering multiple solutions. However, it is prone to generate very large solutions, a phenomenon often called “bloat”. The bloated solutions are not easily understandable. In this paper, we propose two techniques for simplifying the generated models. Both techniques are tested by generating models for a well-known psychology experiment. The validity of these techniques is further tested by applying them to a symbolic regression problem. Several population dynamics are studied to make sure that these techniques are not compromising diversity – an important measure for finding better solutions. The results indicate that the two techniques can be both applied independently and simultaneously and that they are capable of finding solutions at par with those generated by the standard GP algorithm – but with significantly reduced program size. There was no loss in diversity nor reduction in overall fitness. In fact, in some experiments, the two techniques even improved fitness.

Item Type:	Book Section
Official URL:	https://dl.acm.org/conference/sac
Additional Information:	© 2021 ACM, Inc
Divisions:	CPNSS
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	15 Apr 2021 12:51
Last Modified:	20 Dec 2024 00:18
URI:	http://eprints.lse.ac.uk/id/eprint/109876

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