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The fundamental premise of Natural Computing is that nature computes, and that computing capability has to be understood, modeled, abstracted and used for different objectives and in different contexts. Therefore, it is necessary to propose a new language capable of describing and allowing the comprehension of natural systems as a union of computing(More)
Many combinatorial optimization problems belong to the NP class and, thus, cannot be solved optimally in feasible time using standard techniques (e.g., enumeration methods). NP problems have been tackled with some success by techniques known as meta-heuristics. The present paper proposes a new meta-heuristics for solving traveling salesman problems (TSP)(More)
Multi-layer perceptron (MLP) neural network training can be seen as a special case of function approximation, where no explicit model of the data is assumed. In its simplest form, it corresponds to finding an appropriate set of weights that minimize the network training and generalization errors. Various methods can be used to determine these weights, from(More)
Choosing the ideal spot to set up an Access Point (AP) in a given plant is no simple task. This paper proposes a mathematical model and computational solution to the following problem: given the plant of an indoor environment, a number of reception points and properly characterized obstacles, locate where the access point should be installed, so as to(More)