For existing approaches that works on an unknown problem, some
recent multiple algorithm portfolio approaches use a common
population and a self adaptive approach to apportion different
algorithms at different stages. Compared with these approaches,
ours use a distinctly different philosophy, namely, we concentrate
on selecting the best algorithm given the current computational
budget and predicted performance. We believe that these two
approaches are complementary, and this paper provides a fresh alternative.