# On Solving the Maximum Betweenness Problem Using Genetic Algorithms

Serdica Journal of Computing (2009)

- Volume: 3, Issue: 3, page 299-308
- ISSN: 1312-6555

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topSavić, Aleksandar. "On Solving the Maximum Betweenness Problem Using Genetic Algorithms." Serdica Journal of Computing 3.3 (2009): 299-308. <http://eudml.org/doc/11363>.

@article{Savić2009,

abstract = {In this paper a genetic algorithm (GA) is applied on Maximum
Betweennes Problem (MBP). The maximum of the objective function is
obtained by finding a permutation which satisfies a maximal number of
betweenness constraints. Every permutation considered is genetically coded
with an integer representation. Standard operators are used in the GA.
Instances in the experimental results are randomly generated. For smaller
dimensions, optimal solutions of MBP are obtained by total enumeration.
For those instances, the GA reached all optimal solutions except one. The
GA also obtained results for larger instances of up to 50 elements and 1000
triples. The running time of execution and finding optimal results is quite
short.},

author = {Savić, Aleksandar},

journal = {Serdica Journal of Computing},

keywords = {Evolutionary Approach; Genetic Algorithms; Betweenness Problem; numerical examples; genetic algorithms; betweenness problem},

language = {eng},

number = {3},

pages = {299-308},

publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},

title = {On Solving the Maximum Betweenness Problem Using Genetic Algorithms},

url = {http://eudml.org/doc/11363},

volume = {3},

year = {2009},

}

TY - JOUR

AU - Savić, Aleksandar

TI - On Solving the Maximum Betweenness Problem Using Genetic Algorithms

JO - Serdica Journal of Computing

PY - 2009

PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences

VL - 3

IS - 3

SP - 299

EP - 308

AB - In this paper a genetic algorithm (GA) is applied on Maximum
Betweennes Problem (MBP). The maximum of the objective function is
obtained by finding a permutation which satisfies a maximal number of
betweenness constraints. Every permutation considered is genetically coded
with an integer representation. Standard operators are used in the GA.
Instances in the experimental results are randomly generated. For smaller
dimensions, optimal solutions of MBP are obtained by total enumeration.
For those instances, the GA reached all optimal solutions except one. The
GA also obtained results for larger instances of up to 50 elements and 1000
triples. The running time of execution and finding optimal results is quite
short.

LA - eng

KW - Evolutionary Approach; Genetic Algorithms; Betweenness Problem; numerical examples; genetic algorithms; betweenness problem

UR - http://eudml.org/doc/11363

ER -

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