Improving gene expression programming using diversity preservation tournament and its application in grid cell modeling

摘要

In gene expression programming, diversity can be reduced during evolution, sometimes resulting in premature convergence because of non-coding regions, leading to substantial reproduction of repeated individuals. In order to increase the diversity of the population and to avoid premature convergence, we propose a new diversity preservation tournament operator, adopting a tree-based similarity measurement and global probability weights. Furthermore, the proposed tournament operator is embedded into a hybrid evolution architecture to search for a parsimonious model for the firing pattern of grid cells, neurons in the mammalian brain involved in navigation. Experimental results demonstrate that the proposed diversity preservation tournament improves the performance of gene expression programming for evolving a model for grid-cell data.