A Preliminary Investigation into Directed Mutations in Evolutionary Algorithms

The traditional mutation operator within evolution strategies and evolutionary programming relies on adding a multivariate zero mean Gaussian random vector to each parent solution. An alternative method is proposed that allows for optimizing the direction of such mutations. The notion of mutation in polar coordinates is adopted such that parents generate offspring in a selected direction with a random step size. Experiments on four functions suggest that the independent adjustment of direction of travel and step size can produce improvements in rate of convergence on some functions.

[1]  M. E. Johnson,et al.  Generalized simulated annealing for function optimization , 1986 .

[2]  J. W. Atmar,et al.  Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems , 1990, Biological Cybernetics.

[3]  Andreas Ostermeier,et al.  An Evolution Strategy with Momentum Adaptation of the Random Number Distribution , 1992, PPSN.

[4]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[5]  Hans-Paul Schwefel,et al.  Numerical optimization of computer models , 1981 .

[6]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[7]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[8]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[9]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[10]  Hans-Michael Voigt,et al.  Modal mutations in evolutionary algorithms , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[11]  H. Bremermann,et al.  AN EVOLUTION-TYPE SEARCH METHOD FOR CONVEX SETS. , 1964 .

[12]  K. Steiglitz,et al.  Adaptive step size random search , 1968 .

[13]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[14]  D. Fogel,et al.  A comparison of methods for self-adaptation in evolutionary algorithms. , 1995, Bio Systems.