On-the-Fly Simplification of Genetic Programming Models

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Javed:2021:SAC,

• author = "Noman Javed and Fernand R. Gobet",
• title = "On-the-Fly Simplification of Genetic Programming Models",
• booktitle = "Proceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021",
• year = "2021",
• series = "SAC '21",
• pages = "464--471",
• address = "Virtual Event, Republic of Korea",
• publisher = "Association for Computing Machinery",
• keywords = "genetic algorithms, genetic programming, simplification, evolutionary computing",
• isbn13 = "9781450381048",
• URL = "https://doi.org/10.1145/3412841.3441926",
• DOI = "doi:10.1145/3412841.3441926",
• 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 high-dimensional 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. 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.",
• notes = "London School of Economics and Political Science",

}

Genetic Programming entries for Noman Javed Fernand Gobet

Citations