Interpretable symbolic regression for data science: analysis of the 2022 competition
Symbolic regression searches for analytic expressions that accurately describe studied
phenomena. The main attraction of this approach is that it returns an interpretable model that …
phenomena. The main attraction of this approach is that it returns an interpretable model that …
SRBench++: principled benchmarking of symbolic regression with domain-expert interpretation
FO de Franca, M Virgolin, M Kommenda… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
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 …
phenomena. The main promise of this approach is that it may return an interpretable model …
Comparing the Expressive Power of Strongly-Typed and Grammar-Guided genetic programming
Since Genetic Programming (GP) has been proposed, several flavors of GP have arisen,
each with their own strengths and limitations. Grammar-Guided and Strongly-Typed GP …
each with their own strengths and limitations. Grammar-Guided and Strongly-Typed GP …
Response to comments on “Jaws 30”
WB Langdon - Genetic Programming and Evolvable Machines, 2023 - Springer
I would like to thank Leonardo Vanneschi and Leonardo Trujillo for the opportunity to lead
their peer commentary on the thirtieth anniversary of John R. Koza's book “Genetic …
their peer commentary on the thirtieth anniversary of John R. Koza's book “Genetic …
Semantically Rich Local Dataset Generation for Explainable AI in Genomics
Black box deep learning models trained on genomic sequences excel at predicting the
outcomes of different gene regulatory mechanisms. Therefore, interpreting these models …
outcomes of different gene regulatory mechanisms. Therefore, interpreting these models …
cp3-bench: A tool for benchmarking symbolic regression algorithms tested with cosmology
ME Thing, SM Koksbang - arXiv preprint arXiv:2406.15531, 2024 - arxiv.org
We present a benchmark study of ten symbolic regression algorithms applied to
cosmological datasets. We find that the dimension of the feature space as well as precision …
cosmological datasets. We find that the dimension of the feature space as well as precision …
Domain-Aware Feature Learning with Grammar-Guided Genetic Programming
Feature Learning (FL) is key to well-performing machine learning models. However, the
most popular FL methods lack interpretability, which is becoming a critical requirement of …
most popular FL methods lack interpretability, which is becoming a critical requirement of …
Comparing Individual Representations in Grammar-Guided Genetic Programming for Glucose Prediction in People with Diabetes
The representation of individuals in Genetic Programming (GP) has a large impact on the
evolutionary process. In this work, we investigate the evolutionary process of three Grammar …
evolutionary process. In this work, we investigate the evolutionary process of three Grammar …
Fundamental, Sentiment and Technical Analysis for Algorithmic Trading Using Novel Genetic Programming Algorithms
E Christodoulaki - 2024 - repository.essex.ac.uk
This thesis explores genetic programming (GP) applications in algorithmic trading,
addressing significant advancements in the field. Investors typically rely on fundamental …
addressing significant advancements in the field. Investors typically rely on fundamental …
A Comparison of Representations in Grammar-Guided Genetic Programming in the context of Glucose Prediction in People with Diabetes
The representation of individuals in Genetic Programming (GP) has a large impact on the
evolutionary process. In this work, we investigate the evolutionary process of three Grammar …
evolutionary process. In this work, we investigate the evolutionary process of three Grammar …