SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is...

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Veröffentlicht in:	IEEE transactions on evolutionary computation 2024, p.1-1
Hauptverfasser:	de Franca, F. O., Virgolin, M., Kommenda, M., Majumder, M. S., Cranmer, M., Espada, G., Ingelse, L., Fonseca, A., Landajuela, M., Petersen, B., Glatt, R., Mundhenk, N., Lee, C. S., Hochhalter, J. D., Randall, D. L., Kamienny, P., Zhang, H., Dick, G., Simon, A., Burlacu, B., Kasak, Jaan, Machado, Meera, Wilstrup, Casper, Cavaz, W. G. La
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Benchmark testing Competition Current measurement Evolutionary computation Interpretable Machine Learning Machine learning algorithms MATHEMATICS AND COMPUTING Prediction algorithms Symbolic Regression Task analysis
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