A solution representation of genetic algorithm for neural network weights and structure

•New solution representation for ANN optimization.•Better performance for the time series prediction problem.•Less complex neural network structure.

This paper presents a new solution representation for genetic algorithm to optimize the neural network model. During the optimization process, the weights, biases and structure of the neural network are considered for altering. The quality of the model is examined by a cost function that deliberates over both minimization of error and complexity of the neural network model. The performance of the proposed method is investigated by applying it on two time series prediction problems. The results show promising results when we compare it with other methods in the literature.