Analysis and Comparison of different Genetic Models for the Clustering problem in Image Analysis
This paper presents several genetic approaches to the clustering problem of N elements in an n-dimensional Feature Space. This process has been applied in an Image Analysis context in order to divide a set of objects into a fixed number of groups, dependending on their characteristics. The partitioning models are based on very general issues so they can be used in many different clustering applications, as well as real objects grouping. The genetic paradigm has been choisen because the cluster Solution Space has to be explored without any ‘a priori’ or heuristic knowledge and also because the performed parallel search can elude the relevant number of local minima in the solution optimisation. Different cluster models and genetic operators have been analysed in order to exploit the genetic algorithm power in an Image Analysis environment. A performance comparison between solutions is shown, using several chromosome codes and genetic operators.
KeywordsGenetic Algorithm Crossover Operator Cluster Problem Uniform Crossover Genetic Paradigm
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