Evolutionary Programming with Operator Adaptation

This paper investigated evolutionary programming with operator adaptation at both population level and individual level. The fitness distributions were employed to update operators at population level while the immediate reward or punishment from the feedback of mutations was applied to change operators at individual level. Experimental results had shown that long jump operators could actually have smaller average winning step sizes. Through observing the evolution of step sizes and fitness distribution values for each mutation operator, it was discovered that small- stepping operator could become the only dominant operator while other more capable operators with long jumps had only been applied at rather low probabilities.