Neuro-Evolutionary Approach to Physics-Aware Symbolic Regression

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00383571" target="_blank" >RIV/68407700:21730/25:00383571 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3712256.3726434" target="_blank" >https://doi.org/10.1145/3712256.3726434</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3712256.3726434" target="_blank" >10.1145/3712256.3726434</a>

Result language

angličtina

Original language name

Neuro-Evolutionary Approach to Physics-Aware Symbolic Regression

Original language description

Symbolic regression is a technique that can automatically derive analytic models from data. Traditionally, symbolic regression has been implemented primarily through genetic programming that evolves populations of candidate solutions sampled by genetic operators, crossover and mutation. More recently, neural networks have been employed to learn the entire analytical model, i.e., its structure and coefficients, using regularized gradient-based optimization. Although this approach tunes the model’s coefficients better, it is prone to premature convergence to suboptimal model structures. Here, we propose a neuro-evolutionary symbolic regression method that combines the strengths of evolutionary-based search for optimal neural network (NN) topologies with gradient-based tuning of the network’s parameters. Due to the inherent high computational demand of evolutionary algorithms, it is not feasible to learn the parameters of every candidate NN topology to the full convergence. Thus, our method employs a memory-based strategy and population perturbations to enhance exploitation and reduce the risk of being trapped in suboptimal NNs. In this way, each NN topology can be trained using only a short sequence of backpropagation iterations. The proposed method was experimentally evaluated on three real-world test problems and has been shown to outperform other NN-based approaches regarding the quality of the models obtained.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

GECCO '25: Proceedings of the Genetic and Evolutionary Computation Conference

ISBN

979-8-4007-1465-8

ISSN

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e-ISSN

—

Number of pages

9

Pages from-to

1264-1272

Publisher name

The ACM Digital Library

Place of publication

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Event location

Malaga

Event date

Jul 14, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001556459900142