Page 75 - 《软件学报》2020年第12期
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张鑫  等:自然进化策略的特征选择算法研究                                                            3741


             3:    Initialize: the number of population genes ϕ
             4:   For population p=1,2,…,σ do
                                                             4
                                                         2
             5:    Initialize population feature sequence with zero  ξ 1 ,ξξ 3 ,ξ
                                                         ,
                                                      p  p  p  p
             6:    Randomly divide the population gene into m Subpopulation
             7:   For subpopulation s=1,2,…,m do
             8:   Initialize distribution parameters  θ (0)
                                            , p s
             9:   End for
             10: End for
             11: Repeat
             12: For generation g=1,2,…,τ do
             13: g ←    g +    1
             14:     For population p=1,2,…,σ do
             15:        For each subpopulation s=1,2,…,m do
             16:           Sample λ individual from distribution  px θ ()g  ) →  x  ,..., x
                                                     ( |
                                                         , ps  1  λ
             17: Feature selection for generated individuals according to Eq.9
             18: Generating random sequences  ξ  according  ξ
                                                    3
                                         4
                                         p
                                                    p
             19: For each individual μ=1,2,…,λ do
                                                            2
                                                                 4
             20:          Local exploration on four feature sequences  ξ 1 p ,ξξ p 3 ,ξ   to generates four new feature sequences
                                                            ,
                                                                p
                                                            p
                       ,ξξ
                      ξ 1    p 2     p 4
                            3
                            ,ξ
                          ,
                       p
                           p
             21: Evaluate four new feature sequences  ξ 1    p 2     4 p
                                              ,ξξ
                                                  3
                                                   ,ξ  using fitness function f(⋅)
                                                 ,
                                                  p
                                              p
             22:              Choose best fitness value on four feature sequences as individual fitness
             23: For each new feature sequences  ξ i   p (1≤≤  4)   do
                                               i
             24: If  f  (ξ i   p ) >  ( f ξ p 3 )   do
                                    i
             25:  ξ ←  1 p  ξξ ←  2 p  ,  2 p  ξ 3 p , ξ ←  3 p  ξ
                                    p
             26: Else If  f  (ξ i   p ) >  ( f ξ p 2 )   do
                             i
             27:  ξ ←  1 p  ξξ ←  2 p  ,  2 p  ξ
                             p
             28:           Else if  f  (ξ i   p ) >  ( f ξ 1 p )   do
                      i
                  1
             29:  ξ ← ξ
                  p
                      p
             30: End for
             31:     End for
             32:          Fitness shaping for each individual fitness value using function u(⋅)
             33:       Calculate log-derivatives ∇ θ logπ(x|θ)
                         1  λ
                               ( )∇
             34:  ∇  J () θ ←  ∑  u x  log (xπ  | ) θ
                  θ
                         λ  g = 1  g  θ   g
             35:       Update distribution parameters using Adam
             36:     End for
             37: End for
             38:    Evaluate all populations performance F(p i )(i=1,2,…,σ) by Equ.12
             39:     IF Eq.13 returns true and F(p i ) has the worst overall performance do
             40:       Reset the exploration ability of p i , Transfer the distribution of p i  to the optimal population and reset
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