A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem

Carlos Castro; Broderick Crawford; Eric Monfroy

doi:10.1007/978-3-642-02298-2_119

A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem

Carlos Castro, Broderick Crawford, Eric Monfroy

SCHOOL OF COMPUTER ENGINEERING

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

We deal with the Balanced Academic Curriculum Problem, a real world problem that is currently part of CSPLIB. We introduce a Genetic Local Search algorithm to solve this problem using two objectives which is a more realistic model than the one we used in our previous research. The tests carried out show that our algorithm obtains better solutions than systematic search techniques in the same amount of time.

Original language	English
Title of host publication	Cutting-Edge Research Topics on Multiple Criteria Decision Making
Subtitle of host publication	20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, Proceedings
Publisher	Springer Verlag
Pages	824-832
Number of pages	9
ISBN (Print)	9783642022975
DOIs	https://doi.org/10.1007/978-3-642-02298-2_119
State	Published - 2009

Publication series

Name	Communications in Computer and Information Science
Volume	35
ISSN (Print)	1865-0929

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10.1007/978-3-642-02298-2_119

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Castro, C., Crawford, B., & Monfroy, E. (2009). A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem. In Cutting-Edge Research Topics on Multiple Criteria Decision Making: 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, Proceedings (pp. 824-832). (Communications in Computer and Information Science; Vol. 35). Springer Verlag. https://doi.org/10.1007/978-3-642-02298-2_119

Castro, Carlos ; Crawford, Broderick ; Monfroy, Eric. / A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem. Cutting-Edge Research Topics on Multiple Criteria Decision Making: 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, Proceedings. Springer Verlag, 2009. pp. 824-832 (Communications in Computer and Information Science).

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abstract = "We deal with the Balanced Academic Curriculum Problem, a real world problem that is currently part of CSPLIB. We introduce a Genetic Local Search algorithm to solve this problem using two objectives which is a more realistic model than the one we used in our previous research. The tests carried out show that our algorithm obtains better solutions than systematic search techniques in the same amount of time.",

author = "Carlos Castro and Broderick Crawford and Eric Monfroy",

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language = "English",

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Castro, C, Crawford, B & Monfroy, E 2009, A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem. in Cutting-Edge Research Topics on Multiple Criteria Decision Making: 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, Proceedings. Communications in Computer and Information Science, vol. 35, Springer Verlag, pp. 824-832. https://doi.org/10.1007/978-3-642-02298-2_119

A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem. / Castro, Carlos; Crawford, Broderick; Monfroy, Eric.
Cutting-Edge Research Topics on Multiple Criteria Decision Making: 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, Proceedings. Springer Verlag, 2009. p. 824-832 (Communications in Computer and Information Science; Vol. 35).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Castro C, Crawford B, Monfroy E. A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem. In Cutting-Edge Research Topics on Multiple Criteria Decision Making: 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, Proceedings. Springer Verlag. 2009. p. 824-832. (Communications in Computer and Information Science). doi: 10.1007/978-3-642-02298-2_119

A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem

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