Excellence in Research and Innovation for Humanity
%0 Journal Article
%A Mehdi Basikhasteh and  Mohamad A. Movafaghpour
%D 2011 
%J  International Journal of Computer, Electrical, Automation, Control and Information Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 57, 2011
%T Hybridizing Genetic Algorithm with Biased Chance Local Search
%U http://waset.org/publications/4749
%V 57
%X This paper explores university course timetabling
problem. There are several characteristics that make scheduling and
timetabling problems particularly difficult to solve: they have huge
search spaces, they are often highly constrained, they require
sophisticated solution representation schemes, and they usually
require very time-consuming fitness evaluation routines. Thus
standard evolutionary algorithms lack of efficiency to deal with
them. In this paper we have proposed a memetic algorithm that
incorporates the problem specific knowledge such that most of
chromosomes generated are decoded into feasible solutions.
Generating vast amount of feasible chromosomes makes the progress
of search process possible in a time efficient manner. Experimental
results exhibit the advantages of the developed Hybrid Genetic
Algorithm than the standard Genetic Algorithm.
%P 1057 - 1062