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Fine Tuning Deep Boltzmann Machines Through Meta-Heuristic Approaches
(Ieee, 2018-01-01)
The Deep learning framework has been widely used in different applications from medicine to engineering. However, there is a lack of works that manage to deal with the issue of hyperparameter fine-tuning, since machine ...
Enhancing Hyper-to-Real Space Projections Through Euclidean Norm Meta-heuristic Optimization
(2021-01-01)
The continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of them remains a challenge due to the overwhelming ...
Genetic algorithm, MIP and improvement heuristic applied to the MLCLP with backlogging
(2013-08-21)
The present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and ...
Meta-heuristic approaches for a soft drink industry problem
(2008-11-24)
The present paper evaluates meta-heuristic approaches to solve a soft drink industry problem. This problem is motivated by a real situation found in soft drink companies, where the lot sizing and scheduling of raw materials ...
Fine-Tuning Temperatures in Restricted Boltzmann Machines Using Meta-Heuristic Optimization
(2020-07-01)
Restricted Boltzmann Machines (RBM) are stochastic neural networks mainly used for image reconstruction and unsupervised feature learning. An enhanced version, the temperature-based RBM (T-RBM), considers a new temperature ...
Fine-Tuning Temperatures in Restricted Boltzmann Machines Using Meta-Heuristic Optimization
(Ieee, 2020-01-01)
Restricted Boltzmann Machines (RBM) are stochastic neural networks mainly used for image reconstruction and unsupervised feature learning. An enhanced version, the temperature-based RBM (T-RBM), considers a new temperature ...
Improving Pre- Trained Weights through Meta - Heuristics Fine- Tuning
(2021-01-01)
Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, ...
Identifying multiple interacting bad data in power system state estimation
(2005-10-31)
This paper presents an intelligent search strategy for the conforming bad data errors identification in the generalized power system state estimation, by using the tabu search meta heuristic. The main objective is to detect ...
Creating classifier ensembles through meta-heuristic algorithms for aerial scene classification
(2020-01-01)
Convolutional Neural Networks (CNN) have been being widely employed to solve the challenging remote sensing task of aerial scene classification. Nevertheless, it is not straightforward to find single CNN models that can ...
Evolutionary multi-move path-relinking for transmission network expansion planning
(2010-12-06)
This paper presents the application of a new metaheuristic algorithm to solve the transmission expansion planning problem. A simple heuristic, using a relaxed network model associated with cost perturbation, is applied to ...