Evolving Evolutionary Algorithms using Linear Genetic Programming
This work addresses the challenge of automating EA design for researchers and practitioners, though it appears incremental as it builds on existing LGP techniques.
The paper tackles the problem of designing Evolutionary Algorithms (EAs) by proposing a model that evolves EAs using Linear Genetic Programming, resulting in evolved algorithms that perform similarly or better than standard approaches on benchmarking problems like function optimization and the Traveling Salesman Problem.
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.