Abstract
An effective algorithm for solving combinatorial optimization problems by using chaotic neurodynamics has already been proposed. Although numerical simulations show that the algorithm is highly efficient, the reason behind its effectiveness has not yet been clarified. In this study, we investigated the searching characteristics of this algorithm for solving combinatorial optimization problems by employing the method of surrogate data, which is frequently used in the field of nonlinear time series analysis. We evaluated how solving abilities depend on bifurcation parameters related to the refractory effects in the chaotic neural networks. Then, we found that the considerable searching ability is decided by refractory effects after neuron firing.
