Evolutionary programming using exponential mutation

In this paper, we present an efficient evolutionary programming with the mutation operator based on double exponential probability distribution concerning the mutation operator of evolutionary programming. There have been proposed various mutation operators by many researchers. However, the mutation operator is mainly based on normal probability distribution or Cauchy probability distribution to evolve solution for given optimization problems. The double exponential probability distribution with one positive real valued parameter has some positive amount variance and is symmetric with respect to origin. Although the variance of this probability distribution is neither infinite as Cauchy distribution, nor unit as standardized normal distribution, the amount of this variance is controllable by the value of this parameter. This fact plays an important role at the evolution process in evolutionary programming. The results of computational experiment show that our proposed evolutionary programming with double exponential probability distribution performs much better than the conventional evolutional programming when applied to the optimization problems which are well known as the benchmark problems in this research field.