A Novel Quantum-Inspired Genetic Algorithm with Expanded Solution Space

In this paper, we present a novel quantum-inspired genetic algorithm with expanded solution space. Based on the double chains quantum genetic algorithm (DCQGA), we have expanded the solution space by increasing the number of solution space transformation functions. And we propose a novel method for quantum rotation gate´s update by using the sign function and the gradient of objective function. With this method we can automatically determine the direction of quantum rotation gate and adaptively adjust the magnitude of quantum rotation gate. Through experimenting on 2 benchmark problem in the optimization literature: Rosenbrock function and Schaffer´s F6 function, we demonstrate that our expanded solution space quantum genetic algorithm (ESSQGA) has achieved more satisfactory results than DCQGA and common genetic algorithm.