144 citations · 367 across the 18 of their papers we have counts for
11 papers · 1 filter
Symbolic Regression is NP-hard
Marco Virgolin, Solon P. Pissis
Symbolic regression (SR) is the task of learning a model of data in the form of a mathematical expression. By their nature, SR models have the potential to be accurate and human-in…
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression
Marco Virgolin, Peter A. N. Bosman
Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A…
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
Marco Virgolin, Eric Medvet, Tanja Alderliesten +1
Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in th…
On genetic programming representations and fitness functions for interpretable dimensionality reduction
Thomas Uriot, Marco Virgolin, Tanja Alderliesten +1
Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not pro…
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten +1
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sortin…
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter +2
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencie…
Contemporary Symbolic Regression Methods and their Relative Performance
William La Cava, Patryk Orzechowski, Bogdan Burlacu +5
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent…
Genetic Programming is Naturally Suited to Evolve Bagging Ensembles
Marco Virgolin
Learning ensembles by bagging can substantially improve the generalization performance of low-bias, high-variance estimators, including those evolved by Genetic Programming (GP). T…
Local Search is a Remarkably Strong Baseline for Neural Architecture Search
T. Den Ottelander, A. Dushatskiy, M. Virgolin +1
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being propos…
On Explaining Machine Learning Models by Evolving Crucial and Compact Features
Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman
Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving n…
Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions
Marco Virgolin, Tanja Alderliesten, Cees Witteveen +1
The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP)…