37 citations · 54 across the 8 of their papers we have counts for
8 papers
Sharing Knowledge without Sharing Data: Stitches can improve ensembles of disjointly trained models
Arthur Guijt, Dirk Thierens, Ellen Kerkhof +3
Deep learning has been shown to be very capable at performing many real-world tasks. However, this performance is often dependent on the presence of large and varied datasets. In s…
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…
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them
Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1
Traditional approaches to neuroevolution often start from scratch. This becomes prohibitively expensive in terms of computational and data requirements when targeting modern, deep…
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…
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…