Evolving evolutionary algorithms using evolutionary algorithms
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
An autonomous GP-based system for regression and classification problems
Applied Soft Computing
Evolutionary design of Evolutionary Algorithms
Genetic Programming and Evolvable Machines
Unconstrained gene expression programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving fitness functions for mating selection
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
On the equivalences and differences of evolutionary algorithms
Engineering Applications of Artificial Intelligence
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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 which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which 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.