M
Marco Aurélio Cavalcanti Pacheco
Researcher at Pontifical Catholic University of Rio de Janeiro
Publications - 179
Citations - 2496
Marco Aurélio Cavalcanti Pacheco is an academic researcher from Pontifical Catholic University of Rio de Janeiro. The author has contributed to research in topics: Genetic programming & Evolutionary algorithm. The author has an hindex of 25, co-authored 178 publications receiving 2265 citations. Previous affiliations of Marco Aurélio Cavalcanti Pacheco include University College London & Jet Propulsion Laboratory.
Papers
More filters
Book
Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms
TL;DR: Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms formally introduces and defines this area of research, presents its main challenges in electronic design, and explores emerging technologies.
Proceedings ArticleDOI
Well Placement Optimization Using a Genetic Algorithm With Nonlinear Constraints
Alexandre A. Emerick,Eugenio Silva,Bruno Messer,Luciana Faletti Almeida,Dilza Szwarcman,Marco Aurélio Cavalcanti Pacheco,Marley M. B. R. Vellasco +6 more
TL;DR: The developed software is the result of a two-year project focused on a robust implementation of a computer-aided optimization tool to deal with realistic well placement problems with arbitrary well trajectories, complex model grids and linear and nonlinear constraints.
Journal ArticleDOI
VLSI architectures for neural networks
TL;DR: An introduction to neural networks and neural information processing is provided, and neurocomputers are discussed, focusing on how their design exploits the architectural properties of VLSI circuits.
Journal ArticleDOI
Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases
L.B. Goncalves,Marley M. B. R. Vellasco,Marco Aurélio Cavalcanti Pacheco,Flávio Joaquim de Souza +3 more
TL;DR: The HNFB/sup -1/ model is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model, which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs.
Journal ArticleDOI
Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother
TL;DR: The present paper reports the current results of the investigations on the use of deep neural networks towards the construction of a continuous parameterization of facies, which can be used for data assimilation with ensemble methods, and shows promising results, outperforming previous methods and generating well-defined channelized facies.