Abstract
A real-valued genetic algorithm is proposed to the optimization problem with continuous variables. It is composed of a simple and general-purpose dynamic scaled fitness and selection operator, real-valued crossover operator, mutation operators and adaptive probabilities for these operators. The proposed algorithm is tested by two generally used functions and is applied to the training of a neural network for image recognition. Experimental results show that the proposed algorithm is an efficient global optimization algorithm.
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