Proceedings ArticleDOI
Prioritized grammar enumeration: symbolic regression by dynamic programming
Tony Worm,Kenneth Chiu +1 more
- pp 1021-1028
TLDR
Prioritized Grammar Enumeration (PGE), a deterministic Symbolic Regression algorithm using dynamic programming techniques, which replaces genetic operators and random number use with grammar production rules and systematic choices and leads the community to new ideas.Abstract:
We introduce Prioritized Grammar Enumeration (PGE), a deterministic Symbolic Regression (SR) algorithm using dynamic programming techniques. PGE maintains the tree-based representation and Pareto non-dominated sorting from Genetic Programming (GP), but replaces genetic operators and random number use with grammar production rules and systematic choices. PGE uses non-linear regression and abstract parameters to fit the coefficients of an equation, effectively separating the exploration for form, from the optimization of a form. Memoization enables PGE to evaluate each point of the search space only once, and a Pareto Priority Queue provides direction to the search. Sorting and simplification algorithms are used to transform candidate expressions into a canonical form, reducing the size of the search space. Our results show that PGE performs well on 22 benchmarks from the SR literature, returning exact formulas in many cases. As a deterministic algorithm, PGE offers reliability and reproducibility of results, a key aspect to any system used by scientists at large. We believe PGE is a capable SR implementation, following an alternative perspective we hope leads the community to new ideas.read more
Citations
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Proceedings ArticleDOI
Multiple regression genetic programming
TL;DR: MRGP's output method is shown to be superior to the output of program execution and it represents a practical, cost neutral, improvement to GP.
Posted Content
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer,Alvaro Sanchez-Gonzalez,Peter W. Battaglia,Rui Xu,Kyle Cranmer,David N. Spergel,Shirley Ho +6 more
TL;DR: In this paper, a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases is proposed. But the approach is restricted to Graph Neural Networks (GNNs).
Journal ArticleDOI
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data
TL;DR: In this paper, a modular approach for distilling hidden flow physics from discrete and sparse observations is proposed, which combines evolutionary computation with feature engineering to provide a tool for discovering hidden parameterizations embedded in the trajectory of fluid flows in the Eulerian frame of reference.
Journal ArticleDOI
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensors
TL;DR: In this paper, a modular approach for distilling hidden flow physics in discrete and sparse observations is proposed, which combines evolutionary computation with feature engineering to provide a tool to discover hidden parameterizations embedded in the trajectory of fluid flows in the Eulerian frame of reference.
Proceedings ArticleDOI
Geometric Semantic Genetic Programming with Local Search
Mauro Castelli,Leonardo Trujillo,Leonardo Vanneschi,Sara Silva,Emigdio Z-Flores,Pierrick Legrand +5 more
TL;DR: The experimental results show that GSGP-LS achieves the best training fitness while converging very quickly, but severely overfits, and suggest that future GSGP algorithms should focus on finding the correct balance between the greedy optimization of a local search strategy and the more robust geometric semantic operators.
References
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Proceedings Article
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Proceedings Article
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
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