Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions
| Autor: | Tanja Alderliesten, Peter A. N. Bosman, Cees Witteveen, Marco Virgolin |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Interleaving Computer science Genetic Linkage Evolutionary algorithm Decision tree Genetic programming 02 engineering and technology Variation (game tree) Machine learning computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering Neural and Evolutionary Computing (cs.NE) Interpretability Linkage (software) GOMEA business.industry Computer Science - Neural and Evolutionary Computing Biological Evolution Semantics Computational Mathematics machine learning 020201 artificial intelligence & image processing Artificial intelligence business Symbolic regression symbolic regression linkage interpretability computer Algorithms |
| Zdroj: | Evolutionary Computation, 29(2), 211-237. MIT PRESS Evolutionary Computation, 29(2), 211-237 |
| ISSN: | 1530-9304 1063-6560 |
| Popis: | The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR. Comment: fix NMSE definition (it is 100xMSE/var instead of MSE/var) |
| Databáze: | OpenAIRE |
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