  Finding the optimal parameter setting (i.e. the optimal population size, the
optimal mutation probability, the optimal evolutionary model etc) for an
Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the
parameters of the algorithm we will evolve an entire EA capable of solving a
particular problem. For this purpose the Multi Expression Programming (MEP)
technique is used. Each MEP chromosome will encode multiple EAs. An
nongenerational EA for function optimization is evolved in this paper.
Numerical experiments show the effectiveness of this approach.
