Машинное обучение 1, осень 2014: Генетические алгоритмы, Differential evolution

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    14-Dec-2014

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<ul><li> 1. . , . , 2014 . , . , -, 2014 . 1 22 </li> <li> 2. 1 2 3 Differential evolution 4 No free lunch theorem . , . , -, 2014 . 2 22 </li> <li> 3. F0 = arg max F p(FjX) + ; F; . . , . , -, 2014 . 3 22 </li> <li> 4. dream team; ; . . , . , -, 2014 . 4 22 </li> <li> 5. . ) ) "" ) ) , "" : . . , . , -, 2014 . 5 22 </li> <li> 6. 1 2 "" 3 1 2 3 "" 4 "" . , . , -, 2014 . 6 22 </li> <li> 7. - / - - - "" "" - "" - . , . , -, 2014 . 7 22 </li> <li> 8. 1 2 "" 3 1 2 3 "" 4 "" . , . , -, 2014 . 8 22 </li> <li> 9. . : [0; 1]n f0; 1gk . , . , -, 2014 . 9 22 </li> <li> 10. - ! : , "" shuffle "" "" ( ) :) . , . , -, 2014 . 10 22 </li> <li> 11. , . ! "" : n-point crossover; cutnsplice; . . , . , -, 2014 . 11 22 </li> <li> 12. . , . , -, 2014 . 12 22 </li> <li> 13. (genetic drift) ) . MCMC/ . . , . , -, 2014 . 13 22 </li> <li> 14. vs - "" , - "" . : , . , , . , . . . , . , -, 2014 . 14 22 </li> <li> 15. % "" "penalty" . , . , -, 2014 . 15 22 </li> <li> 16. pros cons , . : ( :)); ; ; , . : ; ( ); . . , . , -, 2014 . 16 22 </li> <li> 17. . , . , -, 2014 . 17 22 </li> <li> 18. Differential Evolution arg max </li> <li> 19. 2Rn F( </li> <li> 20. ) 1 2 , x 2 P: 1 a; b; c 2 P ; 2 k U(1::n); 3 y = (yi ) i 1 r U((0; 1)) 2 yi = ai + F(bi ci ); i = kjr &lt; C yi = xi 4 , . 3 . . , . , -, 2014 . 18 22 </li> <li> 21. 2 . ? : : : , : Theorem (No free lunch theorem) . , . , -, 2014 . 19 22 </li> <li> 22. NFL: dm = f(dxm (1); dym (1)); : : : ; (dxm (m); dym (m))g f : X ! Y F = YX p(f ) = 1F Theorem (David Wolpert and William G. Macready (1997)) a1 a2: P f p(dym jf ;m; a1) = P f p(dym P jf ;m; a2) f p(dym jf0;M;m; a1) = P f p(dym jf0;M;m; a2) . , . , -, 2014 . 20 22 </li> <li> 23. NFL: :) , . , . , -, 2014 . 21 22 </li> <li> 24. , : ln(x) : . , . , -, 2014 . 22 22 </li> </ul>