Papers by Germano Vasconcelos
Artificial Intelligence and Soft Computing, 2018
Many bio-inspired algorithms have been proposed to solve optimization problems. However, there is... more Many bio-inspired algorithms have been proposed to solve optimization problems. However, there is still no conclusive evidence of superiority of particular algorithms in different problems, diverse experimental situations and varied testing scenarios. Here, eight methods are investigated through extensive experimentation in three problems: (1) benchmark functions optimization, (2) wind energy forecasting and (3) data clustering. Genetic algorithms, ant colony optimization, particle swarm optimization, artificial bee colony, firefly algorithm, cuckoo search algorithm, bat algorithm and self-adaptive cuckoo search algorithm are compared, concerning, the quality of solutions according to several performance metrics and convergence to best solution. A bio-inspired technique for automatic parameter tuning was developed to estimate the optimal values for each algorithm, allowing consistent performance comparison. Experiments with thousands of configurations, 12 performance metrics and Friedman and Nemenyi statistical tests consistently evidenced that cuckoo search works efficiently, robustly and superior to the other methods in the vast majority of experiments.
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ArXiv, 2019
Multi-Focus Image Fusion seeks to improve the quality of an acquired burst of images with differe... more Multi-Focus Image Fusion seeks to improve the quality of an acquired burst of images with different focus planes. For solving the task, an activity level measurement and a fusion rule are typically established to select and fuse the most relevant information from the sources. However, the design of this kind of method by hand is really hard and sometimes restricted to solution spaces where the optimal all-in-focus images are not contained. Then, we propose here two fast and straightforward approaches for image fusion based on deep neural networks. Our solution uses a multiple source Hourglass architecture trained in an end-to-end fashion. Models are data-driven and can be easily generalized for other kinds of fusion problems. A segmentation approach is used for recognition of the focus map, while the weighted average rule is used for fusion. We designed a training loss function for our regression-based fusion function, which allows the network to learn both the activity level measur...
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Pattern Recognition Letters, 2017
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2016 IEEE Congress on Evolutionary Computation (CEC), 2016
Energy produced by wind farms is already responsible for a relevant portion of world's renewa... more Energy produced by wind farms is already responsible for a relevant portion of world's renewable energy and is increasingly becoming more important in modern society. As energy produced by wind farms is highly dependent on wind speed, it is not generated with regular output values requiring good forecasting systems to ensure good electricity supply. Cuckoo Search is a method recently introduced as a promising optimization algorithm for parameter estimation in some aplications and in this work it is investigated in wind energy forecasting. Cuckoo search is compared to other popular nature inspired techniques to predict the output values of wind parks in Texas and Montana, both in the USA. Results showed, with statistical significance, Cuckoo Search (CS) and Self Adaptive Cuckoo Search (SACS) not only presented higher prediction performance when compared to PSO, ACO and a hybrid of both but also converged significantly faster than the other algorithms.
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Anais do 7. Congresso Brasileiro de Redes Neurais, 2016
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2007 IEEE Symposium on Computational Intelligence in Scheduling, 2007
... PDF - Access Full Text A Robust Method for the VRPTW with Multi-Start Simulated Annealing and... more ... PDF - Access Full Text A Robust Method for the VRPTW with Multi-Start Simulated Annealing and Statistical Analysis. 4218617 abstract; Rights And Permissions; de Oliveira, HCB; Vasconcelos, GC; Alvarenga, GB; Mesquita, RV; de Souza, MM; Center for Informatics, Fed. Univ. ...
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The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
Vehicle routing problems have been extensively analyzed to reduce transportation costs of people ... more Vehicle routing problems have been extensively analyzed to reduce transportation costs of people and goods. More particularly, the vehicle routing problem with time windows (VRPTW) imposes the period of time of customer availability as a constraint, a very common characteristic in real world picking up and delivery problems. Using minimization of the total distance as the main objective to be
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ABSTRACT Hybrid Neural Systems that integrate symbolic algorithms or fuzzysystems to Artificial N... more ABSTRACT Hybrid Neural Systems that integrate symbolic algorithms or fuzzysystems to Artificial Neural Networks (ANN) are a potential alternativeto the more traditional ANN models. However, in contrast with the ANNmodels, these systems have not been yet fully explored from a practicalviewpoint to show their effectiveness in large scale applications. Thispaper presents an extensive comparative analysis of the neuro-fuzzymodels FWD (Feature-Weighted Detector) and FuNN (Fuzzy Neural Network),together with their rule extraction techniques in a large-scale problem.Two aspects are considered: generalization performance of the models,and the interpretation and explanation qualities of the extractedknowledge. The experiments are conducted in the context of a large scalecredit risk assessment application in a real-world operation of aBrazilian financial institution. The results attained are compared tothose observed with multi-layer perceptron networks.
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Pattern Recognition Letters, 1995
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Neural networks : the official journal of the International Neural Network Society, 2017
This paper proposes a method to perform time series prediction based on perturbation theory. The ... more This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results foun...
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Papers by Germano Vasconcelos