Structured total least squares based on genetic algorithms

Structured rank-deficient matrices arise in many applications in signal processing. The inverse iteration algorithm was proposed to solve the so-called structured total least squares (STLS) problems. This algorithm, however, converges to local-minimum under certain conditions. It is well known that genetic algorithms are stochastic optimization techniques that can often outperform classical methods of optimization. Genetic algorithms was utilized here to get the better solution of the STLS problems. Computer simulations show that our method ensures convergence to global minimum.