Evolving Evolutionary Algorithms using Multi Expression Programming
This addresses the challenge of parameter tuning in evolutionary computation for researchers and practitioners, though it appears incremental as it builds on existing MEP techniques.
The paper tackled the problem of finding optimal parameter settings for Evolutionary Algorithms (EAs) by evolving entire EAs using Multi Expression Programming (MEP) to solve specific problems, with numerical experiments demonstrating its effectiveness.
Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Multi Expression Programming (MEP) technique is used. Each MEP chromosome will encode multiple EAs. An nongenerational EA for function optimization is evolved in this paper. Numerical experiments show the effectiveness of this approach.