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Great selection pressure genetic algorithm with adaptive operators for adjusting the weights of neural controller

In this paper, capabilities of a feed-forward neural network regarding control of the complex object are investigated. Neural controllers have been trained by a genetic algorithm with adaptive mutation and crossover probabilities. A specific model of aggressive selection operator is proposed along with one way of co-evolution of the crossover and mutation rates. Also, different mechanisms of operator adaptation were compared in sense of resulting controller performance. Finally, the measurement results, taken from the object (hydraulically driven two-joint robot arm) are presented.