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Reseach Article

A Genetic Algorithm based Solution to the Teaching Assignment Problem

by Ian David Wilson, Ross Davies, Nigel Stanton
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 19
Year of Publication: 2013
Authors: Ian David Wilson, Ross Davies, Nigel Stanton
10.5120/14268-0056

Ian David Wilson, Ross Davies, Nigel Stanton . A Genetic Algorithm based Solution to the Teaching Assignment Problem. International Journal of Computer Applications. 81, 19 ( November 2013), 1-6. DOI=10.5120/14268-0056

@article{ 10.5120/14268-0056,
author = { Ian David Wilson, Ross Davies, Nigel Stanton },
title = { A Genetic Algorithm based Solution to the Teaching Assignment Problem },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 19 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number19/14268-0056/ },
doi = { 10.5120/14268-0056 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:26.955939+05:30
%A Ian David Wilson
%A Ross Davies
%A Nigel Stanton
%T A Genetic Algorithm based Solution to the Teaching Assignment Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 19
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Allocation of educators to diverse and rapidly evolving educational programmes of study such as those within Computing and under increasingly tighter budgetary constraints is a non-trivial task. Suitability and availability of expertise coupled with a need to limit disruption to existing teaching assignments can often result in first fit solutions that are less than optimal in terms of suitability. This system is highly sensitive to even small changes, which ripple out through assignments and make it a difficult problem for solution. This paper presents a methodology for profiling programmes of study and, by association, educator expertise that provides a basis for exploring a large number of potential teaching assignments utilising a genetic algorithm. The teaching assignment problem is exponential in problem size and is combinatorially large. Here, a genetic algorithm implementation generates teaching assignments and informs management decision making for continuity planning. The process rapidly achieved very good solutions to a difficult problem, informed scheduling for the coming academic year and determined the acquisition of educators from other areas where local expertise was insufficient for needs.

References
  1. The Joint Taskforce for Computing Curricula 2005. Computing Curricula 2005. ACM & IEEE. , ISBN: 1-59593-359-X.
  2. Holland, J. H. 1975. Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
  3. Turing, A. P. 1948. Intelligent Machinery in: Cybernetics: Key Papers 1968. eds Evans, C. R. and Robertson A. D. J. , University Park Press.
  4. Golberg, D. E. 1989. Genetic Algorithms in search, optimization and machine learning. Addison-Wesley.
  5. Hou, E. S. H, Ansari, N. and Ren, H. 1994. A genetic algorithm for multiprocessor scheduling. Parallel and Distributed Systems, IEEE Transactions on, 5(2), 113-120.
  6. Aickelin, U. and Dowsland, K. A. 2004. An indirect genetic algorithm for a nurse-scheduling problem. Computers & Operations Research, 31(5), 761-778.
  7. Valenzuela, C, Hurley, S. and Smith, D. 1998. A permutation based genetic algorithm for minimum span frequency assignment. In: Parallel Problem Solving from Nature-PPSN V, Springer Berlin Heidelberg, 907-916.
  8. Wilson, I. D. , Jones, A. J. , Jenkins, D. H. and Ware, J. A. , 2005. Predicting housing value: genetic algorithm attribute selection and dependence modelling utilising the Gamma Test. Adv. in Econometrics 19, 243-275.
  9. Wilson, I. D. , Ware, J. M. and Ware J. A. , 2003. A genetic algorithm approach to cartographic map generalization. Computers in Industry 52(3), 291-304.
  10. DeJong, K. A. and Sarma, J. 1993. Generation gaps revisited, Foundations of genetic algorithms 2, D. Whitley, ed. Morgan-Kaufmann, 19-28.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Heuristic Teaching Assignment Combinatorial Optimisation