The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
344 
co is the mutation index, n is the quantity of all the antibodies 
in T (Ab). i is the antibody's index which indicates the order of 
the OIF value in T*(Ab). 
5) Select d As whose F(/1) is max from T*(Ab), and a set T* d 
is composed. T* d is an better solution set, and will be used to 
improve the original population. 
6) Replace d worst individuals from original population S(Ab) 
by T d , and a clonal selection of S(Ab) is completed. 
7) Evaluate the original population S(Ab). If the result reach the 
acceptable domain, S(Ab) is the final result and the bands sets 
in S(Ab) are the best feature. If not, go to the step 2) and do 
another clonal selection on S(Ab) until the evaluate result get 
the given domain. 
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4. RESULTS AND CONCLUSIONS 
Figure 4. Criterion values in original population S( A ) set 
After pretreatment, the experiment select 176 bands from a 
hyperion image data for the input data. Let the size of original 
population be 50, and the length of individuals be 4. The 
experiment is carried out in the MatLab 7.0 environment. When 
(3 is 0.631, co is 0.873, and n is set to 10, d is set to be 8, 
HDRM displays fast convergence. With the increasing of the 
clone selection times, the average OIF value in original set Sn 
increases accelerated (Figure 3). 
1 2 3 4 5 6 7 8 9 10 
iterated times 
Figure 3. The evaluation result for feasible solution set in clonal 
selection progress 
Figure 5. Criterion values in population S(/t) set after 10 
iterated times 
When S(/l) is clonal selected for 10 times, its individuals have 
gotten to nice convergence (Figure 4 and Figure 5). In Figure 4, 
the individuals are partial regular, because they were arranged 
during the clone selection progress. 
It could help us faster solving a feature selection problem by 
involving the HDRM. The effect of indexes in HDRM is shown 
clearly during the experiment. M which is the size of the 
original population decides the calculating size during the 
progress. 
(3 is the increment index. When it increases, the iterated times 
decreasing obviously, but the time calculation cost increasing 
much. So, it is needed to be set to an adaptable value. This 
paper set it 0.631 through experiment. 
co is the mutate index. If it is too larger, the random possibility 
increases. The information original individuals contains could 
be loss too much. If it is too smaller, its search will be too 
partial, and can not to reach the global domain.