Paper Info
Title
Evolutionary Non-linear Great Deluge for University Course Timetabling
Abstract
This paper presents a hybrid evolutionary algorithm to tackle university course timetabling problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. That initialisation process is capable of producing feasible solutions even for the large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conduct experiments to evaluate the performance of the proposed hybrid algorithm and in particular, the contribution of the evolutionary operators. Our results show that the hybrid between non-linear great deluge and evolutionary operators produces very good results on the instances of the university course timetabling problem tackled here.
Year
DOI
Venue
2009
10.1007/978-3-642-02319-4_32
HAIS
Keywords
Field
DocType
feasible timetable,steady-state evolutionary process,university course timetabling,initialisation process,proposed hybrid algorithm,tailored process,hybrid evolutionary algorithm,evolutionary operator,evolutionary non-linear great deluge,non-linear great deluge algorithm,feasible solution,university course,assignment problem,steady state,hybrid algorithm
Population,Mathematical optimization,Hybrid algorithm,Nonlinear system,Evolutionary algorithm,Computer science,Great Deluge algorithm,Heuristics,Local search (optimization),Evolutionary programming
Conference
Volume
ISSN
Citations 
5572
0302-9743
19
PageRank 
References 
Authors
1.04
8
2
Name
Order
Citations
PageRank
Dario Landa Silva131628.38
Joe Henry Obit2293.13