Evolutionary programming based on non-uniform mutation

A new evolutionary programming algorithm (NEP) using the non-uniform mutation operator instead of Gaussian or Cauchy mutation operators is proposed. NEP has the merits of “long jumps” of the Cauchy mutation operator at the early stage of the algorithm and “fine-tunings” of the Gaussian mutation operator at the later stage. Comparisons with the recently proposed sequential and parallel evolutionary algorithms are made through comprehensive experiments. NEP significantly outperforms the adaptive LEP for most of the benchmarks. NEP outperforms some parallel GAs and performs comparably to others in terms of the solution quality and algorithmic robustness. We give a detailed theoretical analysis of NEP. The probability convergence is proved. The expected step size of the non-uniform mutation is calculated. Based on this, the key property of NEP with “long jumps” at the early stage and “fine-tunings” at the later stage is proved strictly.