123 citations · 282 across the 44 of their papers we have counts for
24 papers · 1 filter
The Pitfalls and Potentials of Adding Gene-invariance to Optimal Mixing
Anton Bouter, Dirk Thierens, Peter A. N. Bosman
Optimal Mixing (OM) is a variation operator that integrates local search with genetic recombination. EAs with OM are capable of state-of-the-art optimization in discrete spaces, of…
Improving the efficiency of GP-GOMEA for higher-arity operators
Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten +1
Deploying machine learning models into sensitive domains in our society requires these models to be explainable. Genetic Programming (GP) can offer a way to evolve inherently inter…
A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA
Anton Bouter, Peter A. N. Bosman
Exploiting knowledge about the structure of a problem can greatly benefit the efficiency and scalability of an Evolutionary Algorithm (EA). Model-Based EAs (MBEAs) are capable of d…
The Impact of Asynchrony on Parallel Model-Based EAs
Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1
In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the al…
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…
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models
E. M. C. Sijben, T. Alderliesten, P. A. N. Bosman
Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic. The goal of XAI is to gain trust in a machine learning (ML) model through clear insi…
Adaptive Objective Configuration in Bi-Objective Evolutionary Optimization for Cervical Cancer Brachytherapy Treatment Planning
Leah R. M. Dickhoff, Ellen M. Kerkhof, Heloisa H. Deuzeman +3
The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been proven effective and efficient in solving real-world problems. A prime exampl…
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting
Anton Bouter, Peter A. N. Bosman
In a Gray-Box Optimization (GBO) setting that allows for partial evaluations, the fitness of an individual can be updated efficiently after a subset of its variables has been modif…
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…
Solving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA
Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1
Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can captu…
Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization
Renzo J. Scholman, Anton Bouter, Leah R. M. Dickhoff +2
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-ob…
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…
Heed the Noise in Performance Evaluations in Neural Architecture Search
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise.…
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…
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning
Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness va…
Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering
S. C. Maree, T. Alderliesten, P. A. N. Bosman
In model-based evolutionary algorithms (EAs), the underlying search distribution is adapted to the problem at hand, for example based on dependencies between decision variables. Hi…
Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing
S. C. Maree, T. Alderliesten, P. A. N. Bosman
Domination-based multi-objective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods however stagnate when the majority of th…
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…
Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization
S. C. Maree, T. Alderliesten, P. A. N. Bosman
This report presents benchmarking results of the Hill-Valley Evolutionary Algorithm version 2019 (HillVallEA19) on the CEC2013 niching benchmark suite under the restrictions of the…
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)…
Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering
S. C. Maree, T. Alderliesten, D. Thierens +1
Model-based evolutionary algorithms (EAs) adapt an underlying search model to features of the problem at hand, such as the linkage between problem variables. The performance of EAs…
Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization
S. C. Maree, T. Alderliesten, D. Thierens +1
This report presents benchmarking results of the latest version of the Hill-Valley Evolutionary Algorithm (HillVallEA) on the CEC2013 niching benchmark suite. The benchmarking foll…