booktitle = "IEEE RIVF International Conference on Computing
Communication Technologies - Research, Innovation, and
Vision for the Future (2015 RIVF)",
title = "A new implementation to speed up Genetic Programming",
year = "2015",
month = jan,
pages = "35--40",
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.",