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Genetic Programming and Evolvable Machines, 2004
Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression.
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Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression.
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Parse-matrix evolution for symbolic regression
Engineering Applications of Artificial Intelligence, 2012 Data-driven model is highly desirable for industrial data analysis in case the experimental model structure is unknown or wrong, or the concerned system has changed.
Changtong Luo, Shao-Liang Zhang
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Artificial bee colony programming for symbolic regression
Information Sciences, 2012 Artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms.
Nurhan Karaboga +2 more
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Simulated annealing for symbolic regression
Proceedings of the Genetic and Evolutionary Computation Conference, 2021Symbolic regression aims to hypothesize a functional relationship involving explanatory variables and one or more dependent variables, based on examples of the desired input-output behavior. Genetic programming is a meta-heuristic commonly used in the literature to achieve this goal.
Daniel Kantor +2 more
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Explaining Symbolic Regression Predictions
2020 IEEE Congress on Evolutionary Computation (CEC), 2020The outgrowing application of machine learning methods has raised a discussion in the artificial intelligence community on model transparency. In the center of this discussion is the question of model explanation and interpretability. The genetic programming (GP) community has systematically pointed out as one of the major advantages of GP the fact ...
Renato Miranda Filho +2 more
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Symbolic Regression-Assisted Offline Data-Driven Evolutionary Computation
IEEE Transactions on Evolutionary ComputationWhen solving optimization problems with expensive or implicit objective functions, evolutionary algorithms (EAs) commonly utilize surrogate models as cost-effective substitutes for evaluation. This category of algorithms is referred to as data-driven EAs
Yuhong Sun +4 more
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A Memetic Algorithm for Symbolic Regression
2019 IEEE Congress on Evolutionary Computation (CEC), 2019This 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.
Sun, Haoyuan, Moscato, Pablo
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Improving Genetic Programming for Symbolic Regression with Equality Graphs
Annual Conference on Genetic and Evolutionary ComputationThe search for symbolic regression models with genetic programming (GP) has a tendency of revisiting expressions in their original or equivalent forms. Repeatedly evaluating equivalent expressions is inefficient, as it does not immediately lead to better
F. D. de França, G. Kronberger
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Multiform Genetic Programming Framework for Symbolic Regression Problems
IEEE Transactions on Evolutionary Computationgenetic programming (GP) is a widely recognized and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which limits their search diversity and increases the likelihood of ...
Jinghui Zhong +4 more
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