Illinois Genetic Algorithms Laboratory

Sastry, K., Xiao, G. (2001)
TR No.: 2001016 | Download PDF | Download PS

Abstract:
This paper presents an efficient cluster optimization algorithm. The
proposed algorithm uses extended compact genetic algorithm (ECGA), one of
the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex
local search. The lowest energy structures of silicon clusters with 4-11
atoms have been successfully predicted. The minimum population size and
total number of function (potential energy of the cluster) evaluations
required to converge to the global optimum with a reliability of 96\% have
been empirically determined and are ${\mathcal{O}}\left(n^{4.2}\right)$
and ${\mathcal{O}}\left(n^{8.2}\right)$ respectively. The results obtained
indicate that the proposed algorithm is highly reliable in predicting
globally optimal structures. However, certain efficiency techniques have
to be employed for predicting structures of larger clusters to reduce the
high computational cost due to function evaluation.