Scalability of using restricted Boltzmann machines for combinatorial optimization
M Probst, F Rothlauf, J Grahl - European Journal of Operational Research, 2017 - Elsevier
Abstract Estimation of Distribution Algorithms (EDAs) require flexible probability models that
can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are …
can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are …
Harmless overfitting: Using denoising autoencoders in estimation of distribution algorithms
M Probst, F Rothlauf - Journal of Machine Learning Research, 2020 - jmlr.org
Estimation of Distribution Algorithms (EDAs) are metaheuristics where learning a model and
sampling new solutions replaces the variation operators recombination and mutation used …
sampling new solutions replaces the variation operators recombination and mutation used …
Denoising autoencoders for fast combinatorial black box optimization
M Probst - Proceedings of the Companion Publication of the 2015 …, 2015 - dl.acm.org
We integrate a Denoising Autoencoder (DAE) into an Estimation of Distribution Algorithm
(EDA) and evaluate the performance of DAE-EDA on several combinatorial optimization …
(EDA) and evaluate the performance of DAE-EDA on several combinatorial optimization …
Deep Boltzmann machines in estimation of distribution algorithms for combinatorial optimization
M Probst, F Rothlauf - arXiv preprint arXiv:1509.06535, 2015 - arxiv.org
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be
efficiently learned and sampled. Deep Boltzmann Machines (DBMs) are generative neural …
efficiently learned and sampled. Deep Boltzmann Machines (DBMs) are generative neural …
Design automation systems. Prospects of development
V Zakharchenko, A Marchenko, V Nenia - Information and Software …, 2017 - Springer
The researches of the design automation systems as systems which themselves manage
their work have been considered in this paper. It has been established that insignificant …
their work have been considered in this paper. It has been established that insignificant …
Generative adversarial networks in estimation of distribution algorithms for combinatorial optimization
M Probst - arXiv preprint arXiv:1509.09235, 2015 - arxiv.org
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be
efficiently learned and sampled. Generative Adversarial Networks (GAN) are generative …
efficiently learned and sampled. Generative Adversarial Networks (GAN) are generative …
[PDF][PDF] МОДЕЛІ ТА ІНФОРМАЦІЙНА ТЕХНОЛОГІЯ ОРГАНІЗАЦІЇ ПРОЦЕСУ АВТОМАТИЗОВАНОГО ПРОЕКТУВАННЯ ОБ'ЄКТІВ МАШИНОБУДУВАННЯ
ВП ЗАХАРЧЕНКО - 2018 - radapm.kname.edu.ua
Дисертаційне дослідження спрямоване на скорочення загальної тривалості виконання
проектувальних робіт за рахунок розробки моделей, а також впровадження на їх основі …
проектувальних робіт за рахунок розробки моделей, а також впровадження на їх основі …