64 citations · 239 across the 24 of their papers we have counts for
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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…
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…
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…
Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
Malte Probst, Franz Rothlauf, Jörn Grahl
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative ne…