Double chains quantum genetic algorithm with application to neuro-fuzzy controller design

This paper proposes a double chains quantum genetic algorithm (DCQGA), and shows its application in designing neuro-fuzzy controller. In this algorithm, the chromosomes are composed of qubits whose probability amplitudes comprise gene chains. The quantum chromosomes are evolved by quantum rotation gates, and mutated by quantum non-gates. For the direction of rotation angle of quantum rotation gates, a simple determining method is proposed. The magnitude of rotation angle is computed by integrating the gradient of the fitness function. Furthermore, a normalized neuro-fuzzy controller (NNFC) is constructed and designed automatically by the proposed algorithm. Application of the DCQGA-designed NNFC to real-time control of an inverted pendulum system is discussed. Experimental results demonstrate that the designed NNFC has very satisfactory performance.

► We propose a quantum genetic algorithm with two genes chain in each chromosome.
► The proposed algorithm has better convergence than the similar algorithms.
► We apply this algorithm to neuro-fuzzy controller design of an inverted pendulum.
► Experiment shows that the designed controller has very satisfactory performance.