Abstract
Grammatical Evolution (GE), which is one of evolutionary computations, can find the function or the executable program or program fragment that will achieve a good fitness value for the given objective function to be minimized. This paper describes the use of the Stochastic Schemata Exploiter (SSE) for improving the convergence property of the original GE. The convergence property of the original GE and the improved GE algorithms is compared in the symbolic regression problem. The results show that the Grammatical Evolution using Stochastic Schemata Exploiter (GE-SSE) has the faster convergence speed than the original GE.
