Implementation of Ant Colony Algorithms in Matlab

Result code in IS VaVaI
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Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Implementation of Ant Colony Algorithms in Matlab
Original language description
Ant colony optimization (ACO) algorithms have been successfully applied to combinatorial optimization tasks especially to data mining classification problem. The ant miner algorithm is based on the behavior of ants in searching of food. Implementation ofACO algorithm in MATLAB is presented in this study. Our experiments have used data sets from the UCI data set repository. We have selected databases with different properties such as number of instances, character of attributes, percentage of missing values or imbalance ratio. An important parameter of ACO algorithm is a heuristic function. It was determined that the selection of heuristic function has large influence on calculation time of the algorithm. In the contribution the influence of heuristicfunction on accuracy of the classification algorithm is discussed.
Czech name
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Czech description
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
20th Annual Conference Proceedings Technical Computing Bratislava 2012
ISBN
978-80-970519-4-5
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
"072/1"-"072/7"
Publisher name
RT Systems
Place of publication
Bratislava
Event location
Bratislava
Event date
Nov 7, 2012
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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