By Pierre Collet, Nicolas Monmarché, Pierrick Legrand, Marc Schoenauer, Evelyne Lutton
This publication constitutes the complaints of the ninth overseas convention on man made Evolution, held in Strasbourg, France, in October 2009.
Read Online or Download Artificial Evolution: 9th International Conference, Evolution Artificielle, EA 2009, Strasbourg, France, October 26-28, 2009. Revised Selected Papers (Lecture ... Computer Science and General Issues) PDF
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Extra resources for Artificial Evolution: 9th International Conference, Evolution Artificielle, EA 2009, Strasbourg, France, October 26-28, 2009. Revised Selected Papers (Lecture ... Computer Science and General Issues)
Once again, to further compare the results, proﬁles are extracted at the centre of each bright area in Fig. 7 (note that a mean ﬁlter is used to reduce the noise level). Fig. 8 shows that these proﬁles are relatively close. The proﬁles of the upper bright areas will be symmetrically similar to those of the lower areas. The radius of each sphere accurately matches the corresponding radius in the reference volume. Also, the diﬀerence of radioactivity is preserved in the reconstructed slices. Test 2.
A survey of evolutionary algorithms for clustering. IEEE Trans. Systems, Man, and Cybernetics Part C 39(2), 133–155 (2009) 16. : A GA-based clustering algorithm for large data sets with mixed and categorical values. In: 5th Int. Conf. on Comp. Intel. Mult. , pp. 102–107. IEEE, Los Alamitos (2003) 17. : Further experimentations on the scalability of the GEMGA. -P. ) PPSN 1998. LNCS, vol. 1498, pp. 315–324. Springer, Heidelberg (1998) 18. : A two-level clustering method using linear linkage encoding.
The radioactivity of one of them is twice as great as the other one’s activity. 25 −5 cm (a) Set up. (b) Sinogram. Fig. 4. Test sinogram Fig. 5 shows examples of tomographic slices reconstructed using our evolutionary method and the OS-EM algorithm. Method 1 consists in incrementing the voxel value of the reconstructed volume for each ﬂy that lies in that voxel. Method 2 consists in accumulating the marginal ﬁtness of each ﬂy whose ﬁtness is positive and that lies in that voxel. The ﬁnal volume data is then normalised between 0 and 1.