4 citations · 6 across the 2 of their papers we have counts for
4 papers
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…
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…
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…