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 program inference, the evaluation of how well a candidate solution solves a certain task is usually a computationally intensive procedure. Most of the time, the evaluation involves either submitting the program to a simulation process or testing its behavior on many input arguments; both situations may turn out to be very time-consuming. Things get worse(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)
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)
Ant colony algorithms are known to have a significant ability of finding high-quality solutions in a reasonable time [2]. However, the computational time of these methods is seriously compromised when the current instance of the problem has a high dimension and/or is hard to solve. In this line, a significant amount of research has been done in order to(More)