[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5370/JEET.2008.3.1.125

Handling a Multi-Tasking Environment via the Dynamic Search Genetic Algorithm

Koh, S.P. (College of Engineering, Universiti Tenaga Nasional, Malaysia)
Aris, I.B. (Faculty of Engineering, Universiti Putra Malaysia)
Bashi, S.M. (Faculty of Engineering, Universiti Putra Malaysia)
Chong, K.H. (Dept. of Physic and Science, Universiti Tunku Abdul Rahman, Malaysia)

Publication Information

Journal of Electrical Engineering and Technology / v.3, no.1, 2008 , pp. 125-129 More about this Journal

Abstract

A new genetic algorithm for the solution of a multi-tasking problem is presented in this paper. The approach introduces innovative genetic operation that guides the genetic algorithm more directly towards better quality of the population. A wide variety of standard genetic parameters are explored, and results allow the comparison of performance for cases both with and without the new operator. The proposed algorithm improves the convergence speed by reducing the number of generations required to identify a near-optimal solution, significantly reducing the convergence time in each instance.

Keywords

Artificial Intelligence; Genetic Algorithm; Multi-Tasking;

Citations & Related Records

Reference

1	Beyer, H. G. and Deb, K., 'On self-Adaptive Features In Real-Parameter Evolutionary Algorithms,' IEEE Transactions on Evolutionary Computation 5(3), 2001. pp. 250-270, 2001 DOI ScienceOn
2	Darwen P., Yao X., 'A Dilemma for Fitness Sharing with a Scaling Function,' In Proceeding of IEEE Conference on Evolutionary Computation, pp. 166-171, 1995
3	Herrera, F., Lozano, M., and Sanchez, A. M., 'Hybrid Crossover Operators for Real-Coded Genetic Algorithms,' An Experimental Study, Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain. 2003
4	Whitley D., 'A Genetic Algorithm Tutorial,'Technical Report CS-93-103, Colorado State University, 1993
5	Goldberg, D. E., 'The Design of Innovation: Lessons From Genetic Algorithms, Lessons for the Real World,' Internal Report 98004, Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Illinois, 1997