Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression

Haider, Christian; França, Fabrício Olivetti de; Kronberger, Gabriel; Burlacu, Bogdan

doi:10.1145/3512290.3528714

Cited by 3 publications

(2 citation statements)

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“…The framework allows the user to choose whether to input the expected domain of each variable or to estimate from the input data. This library is also combined with the shape-constraint 2 library to allow a high-level description of Shape-constraints [4,5] The srtree library 3 is responsible for managing and evaluating the expression trees. It supports optimized evaluation of vectorized data and the calculation of the derivative of any order for a given expression.…”

Section: Tir Frameworkmentioning

confidence: 99%

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Multi-Objective Optimization with Transformation-Interaction-Rational Representation for Symbolic Regression

França

2022

Preprint

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The Transformation-Interaction-Rational is a representation for symbolic regression that limits the search space of function forms to well structured functions enabling the use of efficient numerical coefficient determination. The performance of using Genetic Programming with this representation was substantially better than with its predecessor (Interaction-Transformation) while ranking close to the state-of-the-art on a contemporary Symbolic Regression benchmark. In this same benchmark, we observed that the performance could be further improved with an additional selective pressure on smaller datasets for the expression size, thus creating smaller expressions and generalizing better. This was verified with a penalization term applied to the fitness measure together with heuristic rules to determine what is a small dataset. Possibly a better strategy is to adopt a multi-objective search to find a set of non-dominated solutions taking accuracy and model size in consideration. This will automatically stimulate the preference for simpler models whenever two equally accurate model exists, possibly the need for heuristic rules and penalty functions. In this paper, we extend the evolution library used by TIR to support multi-objective optimization, specifically the NSGA-II algorithm, and apply that to the same benchmark. The results show that the use of multi-objective optimization improves the results on a selection of the benchmarks increasing the overall rank of the algorithm to a second place in the benchmark. However, this strategy is still sensitive to overfitting on small data sets.

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Section: Tir Frameworkmentioning

confidence: 99%

“…These techniques have the advantage of having an extensive set of tools created to find an optimal parameters set. On the other hand, having a fixed form can limit the possible shapes the regression model can fit, limiting their extrapolation capabilities [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization with Transformation-Interaction-Rational Representation for Symbolic Regression

França

2022

Preprint

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Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation

Haider,

de Franca,

Burlacu

et al. 2024

Genetic Programming Theory and Practice XX

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Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimization

de França

2023

Genet Program Evolvable Mach

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Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression

Cited by 3 publications

References 10 publications

Multi-Objective Optimization with Transformation-Interaction-Rational Representation for Symbolic Regression

Multi-Objective Optimization with Transformation-Interaction-Rational Representation for Symbolic Regression

Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation

Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimization

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