64 citations · 239 across the 24 of their papers we have counts for
Showing 2015 · cs.NEShow all
3 papers · 2 filters
cs.NE2015★ 5 cited
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization
Malte Probst
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Generative Adversarial Networks (GAN) are generative n…
cs.NE2015★ 5 cited
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
Malte Probst, Franz Rothlauf
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Deep Boltzmann Machines (DBMs) are generative neural n…
cs.NE2015★ 3 cited
Denoising Autoencoders for fast Combinatorial Black Box Optimization
Malte Probst
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks w…