Douglas Adriano Augusto

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This work presents a new evolutionary ensemble method for data classification, which is inspired by the concepts of <i>bagging</i> and <i>boosting</i>, and aims at combining their good features while avoiding their weaknesses. The approach is based on a distributed multiple-population genetic programming (GP) algorithm which exploits the technique of(More)
In this paper we argue that the performance of evolutionary computation on sequential decision problems strongly depends on the characteristics of the task at hand. On "error-avoidance" tasks, in which the decision process is interrupted every time a bad decision is made, evolutionary methods usually perform well. However, the same is not true for(More)
The present work treats the data classification task by means of evolutionary computation techniques using three ingredients: genetic programming, competitive coevolution, and context-free grammar. The robustness and symbolic/interpretative qualities of the genetic programming are employed to construct classification trees via Darwinian evolution. The(More)
One issue in data classification problems is to find an optimal subset of instances to train a classifier. Training sets that represent well the characteristics of each class have better chances to build a successful predictor. There are cases where data are redundant or take large amounts of computing time in the learning process. To overcome this issue,(More)
A new approach for numerical-constant generation in Grammatical Evolution is presented. Experiments comparing our method with the three most popular methods for constant creation are performed. By varying the number of bits to represent a constant, we can increase our method's precision to the desired level of accuracy, overcoming by a large margin the(More)
OBJECTIVE This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns. METHODS The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline(More)
Early-stage cancer and its interactions with the immune system are still not fully understood. In order to better understand these processes, researchers employ different methods. Simulation and in particular, agent-based simulation (ABS) have been found useful tools for understanding it (Look et al. In a previous study (Figueredo et al., 2013b) we have(More)