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Title: RGFGA: An efficient representation and crossover for grouping genetic algorithms
Authors: Tucker, A
Crampton, J
Swift, S
Keywords: Grouping
genetic algorithm
Publication Date: 2005
Publisher: MIT Press
Citation: Evolutionary Computation 13 (4) , pp.477-499.
Abstract: There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

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