pySRURGS - a python package for symbolic regression by uniform random global search

Towfighi, Sohrab

doi:10.21105/joss.01675

Cited by 3 publications

(3 citation statements)

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“…The commonly used symbolic regression methods are genetic programming and related evolution algorithms, which search for the best mathematical expressions by means of natural selection, with the given input of variables and operators. ,, Recently, other methods have also been proposed, for example, the uniform random global search, , the SISSO (sure independence screening and sparsifying operator), and the sparse regression-related methods. ,, In particular, SISSO has recently demonstrated its efficiency in a number of examples in materials science for learning accurate models that are simple expressions. − ,, It first creates a space of all of the expressions within prescribed complexity using the given mathematical operators and variables. Then, all of the expressions { x i } are used as “basis functions” to describe the target property y = ∑β i x i , followed by sparse regression to simplify the model.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection

Guo

Han

et al. 2022

J. Chem. Theory Comput.

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Symbolic regression offers a promising avenue for describing the structure–property relationships of materials with explicit mathematical expressions, yet it meets challenges when the key variables are unclear because of the high complexity of the problems. In this work, we propose to solve the difficulty by automatically searching for important variables from a large pool of input features. A new algorithm that integrates symbolic regression with iterative variable selection (VS) was designed for optimization of the model with a large amount of input features. Using the recent method SISSO for symbolic regression and random search for variable selection, we show that the VS-assisted SISSO (VS–SISSO) can effectively manage even hundreds of input features that the SISSO alone was computationally hindered, and it fastly converges to (near) optimal solutions when the model complexity is not high. The efficiency of this approach for improving the accuracy of symbolic regression in materials science was demonstrated in the two showcase applications of learning approximate equations for the band gap of inorganic halide perovskites and the stability of single-atom alloy catalysts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection

Guo

Han

et al. 2022

J. Chem. Theory Comput.

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“…These include the artificial ant problem, the even parity problem, and the multiplexer problem. The software is the successor to our earlier work (Towfighi, 2019) but uses a different enumeration algorithm that is much more generalizable whereas our previous algorithm was only suitable for symbolic regression problems.…”

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confidence: 99%

pyGOURGS - global optimization of n-ary tree representable problems using uniform random global search

Towfighi¹

2020

JOSS

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Global optimization problems are ubiquitous to engineering and the sciences. Many such problems are not amenable to analytical techniques and examinations of some potential solutions for these problems often suggest that hill-climbing algorithms would be unable to navigate the jagged and confusing terrain. Despite this, genetic programming is often applied to these problems in the hopes that it will be able to identify high-quality solutions. We suspect that genetic programming would perform no better than random search, in agreement with the No Free Lunch Theorems (Wolpert, Macready, & others, 1997), and we devised this software to allow us to perform uniform random global search, also known as pure random search, on these problems. The challenge lies in creating a system that enumerates all possible solutions, such that we are then able to randomly select from this space of solutions, giving each solution the same probability of being selected.

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Symbolic regression by uniform random global search

Towfighi

2019

SN Appl. Sci.

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Purpose: To compare symbolic regression by genetic programming (SRGP) with symbolic regression by random search (SRRS), a novel method for symbolic regression described herein. Methods: We limit our problem space to N binary trees, m terminals and n functions, then use a dense enumeration of full binary trees to perform uniform random sampling from the set of all permitted equations. We compare a single basic configuration of symbolic regression by genetic programming with symbolic regression by random search using 1000 randomly generated problems. We perform a hyperparameter search with 50 randomly generated symbolic regression problems and 198 randomly generated hyperparameter configurations, examining the performance of SRGP against SRRS. Results: For the single configuration experiment, SRGP outperformed SRRS in 49.0% of problems, random search was best in 26.2% of problems, and there was a tie in 24.8% of problems. Of the cases that were not tied, genetic programming was best in 65.6% of experiments (99% CI, [60.7%, 69.2%]). Of the cases that were not tied in the hyperparameter search, SRGP was best in 44% (99% CI, [41%, 48%]) of cases. The average random configuration of SRGP performs worse than does SRRS. Conclusion: SRGP can outperform SRRS with appropriate hyperparameter selection, but our computations suggest that the average SRGP configuration performs worse than does SRRS.

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pySRURGS - a python package for symbolic regression by uniform random global search

Cited by 3 publications

References 4 publications

Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection

Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection

pyGOURGS - global optimization of n-ary tree representable problems using uniform random global search

Symbolic regression by uniform random global search

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