A stochastic method for controlling the scaling parameters of Cauchy mutation in fast evolutionary programming

The fast evolutionary programming (FEP) introduced the Cauchy distribution into its mutation operator, thus the performances of EP were promoted significantly on a number of benchmark problems. However, the scaling parameter of the Cauchy mutation is invariable, which has become an obstacle for FEP to reach better performance. This paper proposes and analyzes a new stochastic method for controlling the variable scaling parameters of Cauchy mutation. This stochastic method collects information from a group of individuals randomly selected from the population. Empirical evidence validates our method to be very helpful in promoting the performance of FEP.