SOES #77 - Uma arquitetura utilizando IGA e aprendizado de máquina aplicado ao NRP

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Resultados e An´ alises - QP3

Tabela 2: Valores referentes ao score e Pre¸co da Preferˆencia ampliando o peso β utilizando LMS e MLP

β 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

I 50 LMS MLP score PP score PP 115,87 0,0% 115,79 0,0% 115,69 0,16% 115,61 0,16% 115,21 0,57% 115,38 0,36% 113,77 1,85% 114,47 1,15% 111,8 3,64% 112,61 2,82% 109,57 5,75% 110,93 4,38% 107,3 7,99% 109,79 5,46% 104,41 10,98% 107,35 7,86% 102,52 13,02% 106,71 8,51% 101,29 14,39% 105,29 9,97% 99,89 16,00% 104,09 11,24%

I 100 LMS MLP score PP score PP 229,51 0% 229,32 0% 229,13 0,17% 229,39 0,03% 227,59 0,84% 228,33 0,43% 225,46 1,17% 226,66 1,16% 222,95 2,94% 224,87 1,98% 220,11 4,27% 224,16 2,30% 215,49 6,51% 222,38 3,12% 213,71 7,34% 220,81 3,85% 212,94 7,78% 217,7 5,35% 211,57 8,48% 216,33 6,00% 210,9 8,82% 214,54 6,89%

I 150 LMS MLP score PP score PP 320,82 0% 320,72 0% 319,85 0,03% 320,35 0,12% 317,49 1,05% 319,31 0,44% 314,03 2,16% 317,15 1,13% 309,34 3,71% 315,36 1,70% 303,75 5,62% 313,42 2,33% 298,04 7,64% 311,32 3,02% 296,1 8,35% 309,29 3,70% 293,35 9,36% 306,74 4,56% 291,17 10,18% 305,23 5,07% 288,31 11,28% 304,05 5,48%

I 200 LMS MLP score PP score PP 458,53 0% 458,38 0% 452,99 1,22% 457,66 0,16% 448,25 2,29% 455,64 0,60% 437,23 4,87% 453,36 1,11% 428,78 6,95% 451,42 1,54% 416,05 10,21% 449,09 2,07% 416,66 10,05% 447,42 2,45% 404,92 13,24% 444,84 3,04% 402,85 13,82% 444,03 3,23% 395,18 16,03% 441,74 3,77% 391,01 17,27% 438,73 4,48%


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