Convergence analysis on a class of quantum-inspired evolutionary algorithms
Shengqiu Yi, Ming Chen, Zhigao Zeng · 2011
In this article we focus on the theoretical analysis of quantum-inspired evolutionary algorithms with Hϵgate. Applying the theory and analytical techniques in non-homogeneous Markov chains, we obtain the conclusion that quantum-inspired evolutionary algorithms converge in probability under some mild conditions. Moreover, we estimate the convergence rate relating to parameters of algorithms.