HGGASA: An Annealing Grouping Genetic Algorithm for Finding Feasible Timetables

Author

Najafi-Ardabili, A. ; Qarouni-Fard, Danial ; Andalibizadeh, Mohamad-reza ; Ghorbani, Ooldooz ; Sheikhaei, Mohammad-Sadegh ; Mohammadzadeh, Javad

Author_Institution

Dept. of Comput. Sci., Ferdowsi Univ., Mashad, Iran

fYear

2007

fDate

18-20 Nov. 2007

Firstpage

262

Lastpage

266

Abstract

Timetabling is a well-known NP-complete constraint satisfaction problem (CSP) that has been widely studied in the past. In this paper we adopt a modified Genetic Algorithm, better know as Grouping GA and tweaked to suit grouping problems. GGA is further combined with Simulated Annealing (HGGASA) to implement the notion of an acceptance function and improve the performance rate of the algorithm. The results demonstrate a better convergence rate for HGGASA, but not uniformly.

Keywords

computational complexity; constraint theory; genetic algorithms; operations research; simulated annealing; HGGASA; NP-complete constraint satisfaction problem; feasible timetables; grouping genetic algorithm; simulated annealing; Biological cells; Computational modeling; Computer science; Convergence; Encoding; Evolutionary computation; Genetic algorithms; Genetic engineering; Java; Simulated annealing; Grouping Genetic Algorithm; Simulated Annealing; Soft Computing; Timetable;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Information Technology, 2007. IIT '07. 4th International Conference on

Conference_Location

Dubai

Print_ISBN

978-1-4244-1840-4

Electronic_ISBN

978-1-4244-1841-1

Type

conf

DOI

10.1109/IIT.2007.4430501

Filename

4430501