  A new model for evolving Evolutionary Algorithms is proposed in this paper.
The model is based on the Linear Genetic Programming (LGP) technique. Every LGP
chromosome encodes an EA which is used for solving a particular problem.
Several Evolutionary Algorithms for function optimization, the Traveling
Salesman Problem, and the Quadratic Assignment Problem are evolved by using the
considered model. Numerical experiments show that the evolved Evolutionary
Algorithms perform similarly and sometimes even better than standard approaches
for several well-known benchmarking problems.
