|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 60 - Issue 12 |
| Published: December 2012 |
| Authors: R. Lakshmi, K. Vivekanandhan, R. Brintha |
10.5120/9742-4293
|
R. Lakshmi, K. Vivekanandhan, R. Brintha . A New Biological Operator in Genetic Algorithm for Class Scheduling Problem. International Journal of Computer Applications. 60, 12 (December 2012), 6-11. DOI=10.5120/9742-4293
@article{ 10.5120/9742-4293,
author = { R. Lakshmi,K. Vivekanandhan,R. Brintha },
title = { A New Biological Operator in Genetic Algorithm for Class Scheduling Problem },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 60 },
number = { 12 },
pages = { 6-11 },
doi = { 10.5120/9742-4293 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A R. Lakshmi
%A K. Vivekanandhan
%A R. Brintha
%T A New Biological Operator in Genetic Algorithm for Class Scheduling Problem%T
%J International Journal of Computer Applications
%V 60
%N 12
%P 6-11
%R 10.5120/9742-4293
%I Foundation of Computer Science (FCS), NY, USA
This paper describes an innovative approach to solve Class Scheduling problem which is a constraint combinatorial NP hard problem. From the wonders of natural evolution, an important phenomenon of RNA interference induced silencing complex (RISC) can be used as Interference Induced Silencing operator and it is incorporated into the Genetic Algorithm to solve any practical problems like Class Scheduling problem. The aim of this research is to create an automated system for class scheduling problem using Genetic Algorithm to the extent by a new biologically inspired operator, Interference Induced Silencing (IIS) operator that it can be used to set the instant specific preferences to generate the effective time table with the probabilistic operators like crossover and mutation. The framework of the fitness function has considered the hard constraints and the soft constraints. The results were proved to be efficient than the simple Genetic algorithm.