Pole-balancing with different evolved neurocontrollers

Abstract

The paper presents various evolved neurocontrollers for the pole-balancing problem with good benchmark performance. They are small neural networks with recurrent connectivity. The applied evolutionary algorithm, which is not based on genetic algorithms, was designed to evolve neural networks with arbitrary connectivity. It uses no quantization of inputs, outputs or internal parameters, and sets no constraints on the number of neurons. Network topology and parameters like weights and bias terms are developed simultaneously.