Combining the basic principle and characteristics of genetic algorithms with the there basic principles of point cluster selection

which are the selection according to the standard of selection

the selection according to the important meanings

the selection according to the range of distributing and density

we design a model of point cluster selection based on genetic algorithms. Considering that we must do our best to preserve the range of distributing

the principle of arranging

the density of distributing of point cluster

the basic principle of the model of point cluster selection is that divide the point cluster M into a sub-clusters: A 1

A 2

...

A a according to the density

then calculate the preserved amount of every sub-cluster according to the amount of every sub-cluster and the total amount to be preserve

last combine convex hull with genetic algorithms to select. The results of the experiments are compared

one is by the methods of the selection based on genetic algorithms and the other is by the methods of the selection of convex hull. From the experimental output

we can get there conclusions: (1) The model of point cluster selection based on genetic algorithms can be better in the selection of dispersive object. (2) The model can preserve the characteristics of the density and the principles of arrangement well. (3) The model can preserve the outlook of the cluster well.