Genetic Algorithm Approach for Solving the Task Assignment Problem

Savić, Aleksandar, et al. "Genetic Algorithm Approach for Solving the Task Assignment Problem." Serdica Journal of Computing 2.3 (2008): 267-276. <http://eudml.org/doc/11466>.

@article{Savić2008,
abstract = {This research was partially supported by the Serbian Ministry of Science and Ecology under project 144007. The authors are grateful to Ivana Ljubić for help in testing and to Vladimir Filipović for useful suggestions and comments.In this paper a genetic algorithm (GA) for the task assignment problem (TAP) is considered.An integer representation with standard genetic operators is used. Computational results are presented for instances from the literature, and compared to optimal solutions obtained by the CPLEX solver. It can be seen that the proposed GA approach reaches 17 of 20 optimal solutions. The GA solutions are obtained in a quite a short amount of computational time.},
author = {Savić, Aleksandar, Tošić, Dušan, Marić, Miroslav, Kratica, Jozef},
journal = {Serdica Journal of Computing},
keywords = {Evolutionary Approach; Genetic Algorithms; Assignment Problems; Multiprocessor Systems; Combinatorial Optimization; evolutionary approach; genetic algorithms; assignment problems; multiprocessor systems; combinatorial optimization},
language = {eng},
number = {3},
pages = {267-276},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Genetic Algorithm Approach for Solving the Task Assignment Problem},
url = {http://eudml.org/doc/11466},
volume = {2},
year = {2008},
}

TY - JOUR
AU - Savić, Aleksandar
AU - Tošić, Dušan
AU - Marić, Miroslav
AU - Kratica, Jozef
TI - Genetic Algorithm Approach for Solving the Task Assignment Problem
JO - Serdica Journal of Computing
PY - 2008
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 2
IS - 3
SP - 267
EP - 276
AB - This research was partially supported by the Serbian Ministry of Science and Ecology under project 144007. The authors are grateful to Ivana Ljubić for help in testing and to Vladimir Filipović for useful suggestions and comments.In this paper a genetic algorithm (GA) for the task assignment problem (TAP) is considered.An integer representation with standard genetic operators is used. Computational results are presented for instances from the literature, and compared to optimal solutions obtained by the CPLEX solver. It can be seen that the proposed GA approach reaches 17 of 20 optimal solutions. The GA solutions are obtained in a quite a short amount of computational time.
LA - eng
KW - Evolutionary Approach; Genetic Algorithms; Assignment Problems; Multiprocessor Systems; Combinatorial Optimization; evolutionary approach; genetic algorithms; assignment problems; multiprocessor systems; combinatorial optimization
UR - http://eudml.org/doc/11466
ER -