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A Memetic Algorithm for Symbolic Regression

Sun, Haoyuan and Moscato, Pablo (2019) A Memetic Algorithm for Symbolic Regression. In: 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE , Piscataway, NJ, pp. 2167-2174. ISBN 978-1-7281-2153-6. https://resolver.caltech.edu/CaltechAUTHORS:20190815-124757200

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Abstract

This research aims to address the practical difficulties of computational heuristics for symbolic regression, which models data with algebraic expressions. In particular we are motivated by cases in which the target unknown function may be best represented as the ratio of functions. We propose an alternative general approach based on a different representation of mathematical models with an analytic continued fraction representation, from which rational function models can be extracted. A memetic algorithm, which is a paradigm of meta-heuristic optimization based on the evolution of solutions by a set of computational agents, is implemented to generate solutions in this representation. A population of computational agents with problem domain knowledge improves feasible solutions using local search heuristics and produces models that fit the data better. In addition, the agents compete in searching for function models with fewer number of variables. Agent interactions are constrained by a population structure which has been previously used in several successful MAs for other combinatorial optimization problems. We utilize a tree-based population structure to improve the algorithm’s consistency and performance. Data from real-world applications are used to measure the potential of our approach and benchmark its performance against other approaches in symbolic regression.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/CEC.2019.8789889DOIArticle
Additional Information:© 2019 IEEE. This work was supported by The University of Newcastle and the Caltech SURF program. P.M. thanks Wadim Zudilin for a useful discussion about Padé approximants and continued fractions.
Funders:
Funding AgencyGrant Number
University of NewcastleUNSPECIFIED
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Subject Keywords:memetic computing, symbolic regression, continued fractions, multivariate regression, memetic programming, analytic theory of continued fractions
Record Number:CaltechAUTHORS:20190815-124757200
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190815-124757200
Official Citation:H. Sun and P. Moscato, "A Memetic Algorithm for Symbolic Regression," 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 2019, pp. 2167-2174. doi: 10.1109/CEC.2019.8789889
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97922
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:15 Aug 2019 19:53
Last Modified:03 Oct 2019 21:36

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