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C. FERREIRA In E. Lutton, J. A. Foster, J. Miller, C. Ryan, and A. G. B. Tettamanzi, eds., Proceedings of the 4th European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 2278, pages 51-60, Springer-Verlag, Berlin, Germany, 2002.

Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming

Coevolution2 Rule
 

The Coevolution2 rule has a performance of 86.0% and was also discovered using a coevolutionary approach between GA-evolved rules and ICs [8].

The solution to the Coevolution2 rule was easier to find than the solution to the previous rule. For this problem, populations of 50 individuals with chromosomes composed of 9 genes of length 22 each were used. The sub-ETs were linked also by the IF function.

In one run, an intermediate solution with fitness 126 was found by generation 18660. This program was used as seed to initialize another evolutionary epoch. After 23807 generations the following solution was found:

0123456789012345678901
X3ONOD2acaa1332baa3211
R1XuION1ua33aa3321cbc2
RMaDAM3u1bb13cuuc2bubu
RRRA22X23b31a3a3122aab
M3D33NM2b21u22aa31bc2b
AOAII3ac2a3c2ua2u21u33
DXO1DXI1ab23u11ba1bba1
IMXcDMR1ub1bcua231cuu1
MDIMAMO2ubac212c22ccac

which is a perfect solution to the Coevolution2 rule. The sub-ETs encoded by each gene are linked 3-by-3 with IF, forming three big clusters that are, in their turn, linked also 3-by-3 with IF.

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