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Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees
Research article (Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015) · cited 15× · AI/ML
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APA4ort.xyz Knowledge Graph. (2026). Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees. Retrieved May 24, 2026, from https://4ort.xyz/entity/avoiding-overfitting-in-symbolic-regression-using-the-first-order-derivative-of-gp-trees
MLA“Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/avoiding-overfitting-in-symbolic-regression-using-the-first-order-derivative-of-gp-trees.
BibTeX@misc{4ortxyz_avoiding-overfitting-in-symbolic-regression-using-the-first-order-derivative-of-gp-trees_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees}}, year = {2026}, url = {https://4ort.xyz/entity/avoiding-overfitting-in-symbolic-regression-using-the-first-order-derivative-of-gp-trees}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Avoiding Overfitting in Symbolic Regression Using the First Order Derivative of GP Trees — https://4ort.xyz/entity/avoiding-overfitting-in-symbolic-regression-using-the-first-order-derivative-of-gp-trees (retrieved 2026-05-24)
