Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Fechar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

002
Publicações

2018

Assessment of predictive learning methods for the completion of gaps in well log data

Autores
Lopes, RL; Jorge, AM;

Publicação
Journal of Petroleum Science and Engineering

Abstract

2016

An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry

Autores
Lopes, RL; Jahromi, HN; Jorge, AM;

Publicação
Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering, C3S2E '16, Porto, Portugal, July 20-22, 2016

Abstract
The Oil and Gas Exploration & Production (E&P) field deals with high-dimensional heterogeneous data, collected at different stages of the E&P activities from various sources. Over the years different soft-computing algorithms have been proposed for data-driven oil and gas applications. The most popular by far are Artificial Neural Networks, but there are applications of Fuzzy Logic systems, Support Vector Machines, and Evolutionary Algorithms (EAs) as well. This article provides an overview of the applications of EAs in the oil and gas E&P industry. The relevant literature is reviewed and categorised, showing an increasing interest amongst the geoscience community. © 2016 ACM.

2014

Developments on the Regulatory Network Computational Device

Autores
Lopes, R; Costa, E;

Publicação
International Journal of Natural Computing Research

Abstract

2013

Genetic Programming with Genetic Regulatory Networks

Autores
Lopes, RL; Costa, E;

Publicação
GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE

Abstract
Evolutionary Algorithms (EA) approach differently from nature the genotype - phenotype relationship, and this view is a recurrent issue among researchers. Recently, some researchers have started exploring computationally the new comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Regulatory Network (ARN) model. Soon after some variants of the ARN, including different improvements over the base model, were tested. In this paper, we combine two of those alternatives, demonstrating experimentally how the resulting model can deal with complex problems, including those that have multiple outputs. The efficacy and efficiency of this variant are tested experimentally using two benchmark problems that show how we can evolve a controller or an artificial artist.

2013

GEARNet: Grammatical Evolution with Artificial Regulatory Networks

Autores
Lopes, RL; Costa, E;

Publicação
GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE

Abstract
The Central Dogma of Biology states that genes made proteins that made us. This principle has been revised in order to incorporate the role played by a multitude of regulatory mechanisms that are fundamental in both the processes of inheritance and development. Evolutionary Computation algorithms are inspired by the theories of evolution and development, but most of the computational models proposed so far rely on a simple genotype to phenotype mapping. During the last years some researchers advocate the need to explore computationally the new biological understanding and have proposed different gene expression models to be incorporated in the algorithms. Two examples are the Artificial Regulatory Network (ARN) model, first proposed by Wolfgang Banzhaf, and the Grammatical Evolution (GE) model, introduced by Michael O'Neill and Conor Ryan. In this paper, we show how a modified version of the ARN can be combined with the GE approach, in the context of automatic program generation. More precisely, we rely on the ARN to control the gene expression process ending in an ordered set of proteins, and on the GE to build, guided by a grammar, a computational structure from that set. As a proof of concept we apply the hybrid model to two benchmark problems and show that it is effective in solving them.