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pro vyhledávání: '"Guijt, Arthur"'
Traditional approaches to neuroevolution often start from scratch. This becomes prohibitively expensive in terms of computational and data requirements when targeting modern, deep neural networks. Using a warm start could be highly advantageous, e.g.
Externí odkaz:
http://arxiv.org/abs/2403.14224
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 b
Externí odkaz:
http://arxiv.org/abs/2303.15543
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 l
Externí odkaz:
http://arxiv.org/abs/2203.05970
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulation-based optimisation. In the literature, these algorithms are usually
Externí odkaz:
http://arxiv.org/abs/2106.04618
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference Companion 2021
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in
Externí odkaz:
http://arxiv.org/abs/2006.04508
Publikováno v:
In Applied Soft Computing November 2023 147
Publikováno v:
Computers & Industrial Engineering, volume 138, article 106102, 2019
The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling. This problem consists of simultaneously selecting a subset of orders to be processed as well as determ
Externí odkaz:
http://arxiv.org/abs/1910.01982
Publikováno v:
In Swarm and Evolutionary Computation April 2022 70