A mutation and crossover adaptation mechanism for differential evolution algorithm
Johanna Aalto, Jouni Lampinen · 2014
A new adaptive Differential Evolution algorithm called EWMA-DECrF is proposed. In original Differential Evolution algorithm three different control parameter values must be pre-specified by the user a priori; Population size, crossover and mutation scale factor. Choosing good parameters can be very difficult for the user, especially for the practitioners. In the proposed algorithm the mutation scale factor and crossover factor is adapted using a mechanism based on exponential weighting moving average, while the population size is kept fixed as in standard Differential Evolution. The algorithm was evaluated by using the set of 25 benchmark functions provided by CEC2005 special session on real-parameter optimization. It was compared to standard DE/rand/1/bin version and the two other algorithms also based on exponential weighting moving average; EWMA-DE and EWMA-DECr. Results show that proposed algorithm EWMA-DECrF outperformed the other algorithms by its average ranking based on normalized success performance.