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Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key(More)
We show in this paper an original version of a classifier system using neural networks as its classifiers. The main point is to determine whether neural networks, as universal approximators, can enrich classifier systems performance. To give a proper answer to this inquiry, the research was divided into two phases. The results presented here are related to(More)
49 In Darwin's time, most geologists subscribed to " catastrophe theory " : that the Earth would be punished many times over by floods, earthquakes and other catastrophes, able to destroy all forms of life. On his voyage on board the Beagle, Darwin verified that the diverse animal species of a region differed from each other in minimal details, but he did(More)
In this paper we introduce the use of contextual transformation functions to adjust membership functions in fuzzy systems. We address both linear and nonlinear functions to perform linear or nonlinear context adaptation, respectively. The key issue is to encode knowledge in a standard frame of reference, and have its meaning tuned to the situation by means(More)
In this paper we introduce a fuzzy elevator group controller using a linear context adaptation technique. We first describe the elevator group control problem and the schemes usually employed to solve it. We detail the fuzzy controller used in our development and an example system used in simulation experiments. The focus is on the comparison between the(More)
– This paper describes a hierarchical evolutionary technique developed to design and train feedforward neural networks with different activation functions on their hidden layer neurons (Heterogeneous Neural Networks). At an upper level, a genetic algorithm is used to determine the number of neurons in the hidden layer and the type of the activation function(More)