Evolving Evolutionary Algorithms with Patterns
This work addresses the challenge of algorithm design for researchers in evolutionary computation, but it appears incremental as it builds on existing techniques like MEP and standard evolutionary schemes.
The paper tackled the problem of automating the design of Evolutionary Algorithms (EAs) by proposing a model that evolves evolutionary patterns using Multi Expression Programming, and the result showed that the evolved algorithms could compete with a human-designed Genetic Algorithm on several benchmarking problems.
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.