RGFGA: an efficient representation and crossover for grouping genetic algorithms.
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.
Tucker
Allan
A
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK. allan.tucker@brunel.ac.uk
Crampton
Jason
J
Swift
Stephen
S
D000465:Algorithms; D005075:Biological Evolution; D002965:Classification; D003198:Computer Simulation; D008957:Models, Genetic; D009154:Mutation