An efficient memetic genetic programming framework for symbolic regression
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An efficient memetic genetic programming framework for symbolic regression Tiantian Cheng1 · Jinghui Zhong1 Received: 7 November 2019 / Accepted: 21 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Background Symbolic regression is one of the most common applications of genetic programming (GP), which is a popular evolutionary algorithm in automatic computer program generation. Despite existing success of GP on symbolic regression, the accuracy and efficiency of GP can still be improved especially on complicated symbolic regression problems, enabling GP to be applied to more fields. Purpose This paper proposes a novel memetic GP framework to improve the accuracy and search efficiency of GP on complicated symbolic regression problems. The proposed framework consists of two components: feature construction and feature combination. The first component focuses on constructing diverse features. The second component aims to filter redundant features and linearly combines these independent features. Methods The first component (feature construction) focuses on constructing polynomial features derived from polynomial functions, and evolves features by a GP solver. In addition, a gradient-based nonlinear least squares algorithm named Levenberg-Marquardt (LM) is embedded in the second component (feature combination) to locally adjust the weights of independent features. A filtering mechanism is put forward to discard redundant features in the second component. Hence, the polynomial features and evolved features can work together in the framework to improve the performance of GP. Results Experimental results demonstrate that the proposed framework offers enhanced performance compared with several state-of-the-art algorithms in terms of accuracy and search efficiency on nine benchmark regression problems and three real-world regression problems. Conclusion In this study, a novel memetic genetic programming framework is proposed to improve the performance of GP on symbolic regression. Experimental results demonstrate that the proposed framework can improve the accuracy and search efficiency of GP on complicated symbolic regression problems compared with four state-of-the-art algorithms. Keywords Evolutionary computation · Genetic programming · Symbolic regression
1 Introduction Genetic programming (GP) is a popular evolutionary algorithm which has been proved quite effective in automatic computer program generation [13,20]. In GP, computer programs are represented as trees and evolved using genetic operators such as crossover and mutation. In the past decades, GP has been developing rapidly and a number of enhanced
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Jinghui Zhong [email protected] Tiantian Cheng [email protected]
1
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
GP variants have been proposed such as gene expression programming (GEP) [15,54,55], cartesian genetic programming (CGP) [26], linear genetic programming (LGP) [6], and others [1,3
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