Scaling Up Evolutionary Programming Algorithms

摘要

Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four different EP algorithms for large-scale problems, i.e., problems whose dimension ranges from 100 to 300. The four are classical EP (CEP) [1, 2], fast EP (FEP) [3], improved FEP (IFEP) [4] and a mixed EP (MEP) proposed in this paper. It is discovered that neither CEP nor FEP performs satisfactorily for the large-scale problems investigated here. However, IFEP and MEP are able to perform consistently well for both unimodal and multimodal functions with various dimensionalities. In addition, the time used by IFEP and MEP to find a near optimal solution appears to grow only polynomially (second-order polynomial) as the dimensionality of the problems studied increases.