Rank Aggregation using Multi Objective Genetic Algorithm

Rank Aggregation is needed to combine many different rank orderings on the same set of alternatives, or candidates, in order to obtain a better ordering. The aim of this field is to somehow merge a number of ranked lists in order to build a single superior ranked list. Various methods exist for deal...

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Bibliographic Details
Published in2015 1st International Conference on Next Generation Computing Technologies (NGCT) pp. 836 - 840
Main Authors Kaur, Manjeet, Kaur, Parneet, Singh, Manpreet
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2015
Subjects
Engines
Genetic algorithms
Genetics
Mean
Metasearch
Rank Aggregation
Search problems
Sociology
Statistics
Stuart
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Summary:Rank Aggregation is needed to combine many different rank orderings on the same set of alternatives, or candidates, in order to obtain a better ordering. The aim of this field is to somehow merge a number of ranked lists in order to build a single superior ranked list. Various methods exist for dealing with the problem of Rank Aggregation problem. In this paper, Rank Aggregation is implemented using Genetic Approach. Multiple objectives have been achieved using genetic approach. So, this approach is called Multi-Objective Genetic Algorithm. The results of the genetic Approach are compared with that of Stuart method and Mean Method. From the experiments, It is concluded that Performance of GA lies between the Sturat and Mean method. In most cases, Stuart method gives better results than GA and GA gives better results than Mean method.
DOI:10.1109/NGCT.2015.7375237