Emergence; Probability vector; Modularity

This method is based on the article [1]. We used the probability vector that helps to construction of n ? bit vectors, e.g. individuals in the population. All individuals in every generation are randomly generated from the probability vector that is associated with this generation. The probability vector is updated on the basis of best individuals in a population in order that it was near upon a best solution with every next generation. The process is repeated until the probability vector entries are close to zero or to one. The resulting probability vector then determines an optimal solution of the given optimalization task. This method is based on principles of evolutionary algorithms and it builds a modular architecture of the neural network. The modular network for a given task emerges as a winning architecture obtained by interference between modules.

This method is based on the article [1]. We used the probability vector that helps to construction of n ? bit vectors, e.g. individuals in the population. All individuals in every generation are randomly generated from the probability vector that is associated with this generation. The probability vector is updated on the basis of best individuals in a population in order that it was near upon a best solution with every next generation. The process is repeated until the probability vector entries are close to zero or to one. The resulting probability vector then determines an optimal solution of the given optimalization task. This method is based on principles of evolutionary algorithms and it builds a modular architecture of the neural network. The modular network for a given task emerges as a winning architecture obtained by interference between modules.