A New Strategy for Gene Expression Programming and Its Applications in Function Mining
Author:
Yongqiang ZHANG ; Jing XIAO
Abstract:
Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrease generations, to make the population maintain diversification throughout the evolutionary process and avoid “premature” and to ensure the convergence ability and evolution efficiency. The simulation experiments show that GEP-PDS can increase the population average fitness by 10% in function mining, and decrease the generations for convergence to the optimal solution by 30% or more compared with other improved GEP.
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