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A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods
(Scitepress, 2017-01-01)
The fine-tuning of the algorithms parameters, specially, in metaheuristics, is not always trivial and often is performed by ad hoc methods according to the problem under analysis. Usually, incorrect settings influence both ...
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 ...
A statistical approach for the fine-tuning of metaheuristics: A case study combining design of experiments and racing algorithms
(2015-01-01)
The fine-tuning of heuristics and metaheuristics exercises a great influence in both the solution process, as well as in the quality of results of optimization problems. The search for the best fit of these algorithms is ...
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 ...
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 ...
Multi-start metaheuristic for transmission system expansion planning using a transportation model
(2018-06-25)
This work presents a multi-start metaheuristic for transmission system expansion planning. The transmission network is modelled as a transportation network. The method progresses in two stages: constructive phase and local ...
Quaternionic flower pollination algorithm
(2017-01-01)
Metaheuristic-based optimization techniques offer an elegant and easy-to-follow framework to optimize different types of problems, ranging from aerodynamics to machine learning. Though such techniques are suitable for ...
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 ...