  A new model for evolving Evolutionary Algorithms (EAs) is proposed in this
paper. The model is based on the Multi Expression Programming (MEP) technique.
Each MEP chromosome encodes an evolutionary pattern that is repeatedly used for
generating the individuals of a new generation. The evolved pattern is embedded
into a standard evolutionary scheme that is used for solving a particular
problem. Several evolutionary algorithms for function optimization are evolved
by using the considered model. The evolved evolutionary algorithms are compared
with a human-designed Genetic Algorithm. Numerical experiments show that the
evolved evolutionary algorithms can compete with standard approaches for
several well-known benchmarking problems.
