[HTML][HTML] Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search
Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based
estimation of distribution genetic programming approach that uses denoising autoencoder …
estimation of distribution genetic programming approach that uses denoising autoencoder …
Addressing design preferences via auto-associative connectionist models: Application in sustainable architectural Façade design
I Chatzikonstantinou, IS Sariyildiz - Automation in construction, 2017 - Elsevier
Truly successful designs are characterized by both satisfaction of design goals and the
presence of desirable physical features. Experienced design professionals are able to …
presence of desirable physical features. Experienced design professionals are able to …
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 …
Deep optimisation: Solving combinatorial optimisation problems using deep neural networks
Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs)
in a novel way-not for optimising a learning algorithm, but for finding a solution to an …
in a novel way-not for optimising a learning algorithm, but for finding a solution to an …
Expanding variational autoencoders for learning and exploiting latent representations in search distributions
In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best
solutions incorporated latent or hidden variables to the models as a more accurate way to …
solutions incorporated latent or hidden variables to the models as a more accurate way to …
Using denoising autoencoder genetic programming to control exploration and exploitation in search
D Wittenberg - European Conference on Genetic Programming (Part …, 2022 - Springer
Abstract Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-
based estimation of distribution genetic programming (EDA-GP) algorithm that uses …
based estimation of distribution genetic programming (EDA-GP) algorithm that uses …
DAE-GP: denoising autoencoder LSTM networks as probabilistic models in estimation of distribution genetic programming
Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics
where sampling new solutions from a learned probabilistic model replaces the standard …
where sampling new solutions from a learned probabilistic model replaces the standard …
Gray-box optimization and factorized distribution algorithms: where two worlds collide
R Santana - arXiv preprint arXiv:1707.03093, 2017 - arxiv.org
The concept of gray-box optimization, in juxtaposition to black-box optimization, revolves
about the idea of exploiting the problem structure to implement more efficient evolutionary …
about the idea of exploiting the problem structure to implement more efficient evolutionary …
Deep optimisation: multi-scale evolution by inducing and searching in deep representations
The ability of evolutionary processes to innovate and scale up over long periods of time,
observed in nature, remains a central mystery in evolutionary biology, and a challenge for …
observed in nature, remains a central mystery in evolutionary biology, and a challenge for …
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 …