A new implementation to speed up Genetic Programming

Abstract

Genetic Programming (GP) is an evolutionary algorithm inspired by the evolutionary process in biology. Although, GP has successfully applied to various problems, its major weakness lies in the slowness of the evolutionary process. This drawback may limit GP applications particularly in complex problems where the computational time required by GP often grows excessively as the problem complexity increases. In this paper, we propose a novel method to speed up GP based on a new implementation that can be implemented on the normal hardware of personal computers. The experiments were conducted on numerous regression problems drawn from UCI machine learning data set. The results were compared with standard GP (the traditional implementation) and an implementation based on subtree caching showing that the proposed method significantly reduces the computational time compared to the previous approaches, reaching a speedup of up to nearly 200 times.