Searched for: subject%3A%22Algorithm%22
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Guijt, Arthur (author), Thierens, Dirk (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
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 algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators...
conference paper 2023
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Guijt, Arthur (author), Thierens, Dirk (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
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 capture the exploitable structure of a problem. Usually, a single type of linkage structure is attempted to be captured using models such as a linkage tree. However, in...
conference paper 2022
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Dushatskiy, A. (author), Lowe, Gerry (author), Bosman, P.A.N. (author), Alderliesten, T. (author)
Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowledged to still be a problem. One way to automatically overcome this is to capture and exploit this variation explicitly. Here, we propose an approach that improves...
conference paper 2022
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Scholman, R.J. (author), Bouter, Anton (author), Dickhoff, Leah R.M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
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-objective Optimization Problem (MMOP), there is a risk that the found set of solutions is not smoothly navigable because the solutions belong to various niches,...
conference paper 2022
document
Dushatskiy, A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt the resulting algorithm for solving non-binary...
conference paper 2021
Searched for: subject%3A%22Algorithm%22
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