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4、Python代码: Z% m. F l. U/ q
#-*- coding:utf-8 -*-9 G! R9 \" _0 n& u! k* k* k0 @
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import random 0 X' E5 U, j* J- U8 mimport math1 [) b: D! i+ K/ V; G0 {6 b& b
from operator import itemgetter + Y- T) o, c s8 P. I" f4 c4 ^
class Gene: # F. s* B7 b5 @0 {- V# u '''5 f5 R* o9 L2 d: A/ {* k
This is a class to represent individual(Gene) in GA algorithom ' Z% X. ]& T! V# M each object of this class have two attribute: data, size : K" \% R3 e! S '''* i# x3 F$ B" |( U* P# _
def __init__(self,**data): + m; D5 }3 K# E& F4 c self.__dict__.update(data) & F6 R% d- A; c0 _# H
self.size = len(data['data'])#length of gene / I; M; t x) }' J9 z# l* D5 ? 2 ~4 E [& B4 _% o5 P4 Z' [% w3 U; x) J0 w! \ |: t% ~+ a
class GA:) P8 Z7 w, E# e: v; d( T8 z
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This is a class of GA algorithm. , a3 }+ _: t; P! D8 E U+ q '''& v4 @3 w% m* D+ S+ g
def __init__(self,parameter):0 j1 U+ ^ Y) F+ D z
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Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value . ' M- U! ~; A) A The data structure of pop is composed of several individuals which has the form like that: 6 N/ [) y9 W) B& h" B2 k; Y - {; ] W* U) f* v8 k {'Gene':a object of class Gene, 'fitness': 1.02(for example)}% g( @9 R! F) ]0 S8 Y+ G0 F" [7 H
Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2], T) b7 V9 i0 \
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'''0 V- c# ?: a% }& P/ z, I' n
#parameter = [CXPB, MUTPB, NGEN, popsize, low, up]# {4 a2 h+ ^; u1 P
self.parameter = parameter * e& j+ }1 V+ j7 D, G9 q {+ e % ?# |1 p1 n5 P$ l) V- L low = self.parameter[4]# C& f) \4 Q% Y: @1 u' B0 e
up = self.parameter[5] 0 E- ] [$ a% f4 u7 F: e* d/ R2 A$ n$ e$ {4 ~ G" S3 }' t" _( Z
self.bound = []3 \/ J4 L. ?, W! L6 D* e5 `9 V1 X
self.bound.append(low) ! H7 H) e# D8 p: h# T+ |! B1 q6 ^ self.bound.append(up) * n" k3 M# ?; t* F$ n2 j! s0 S( I ?4 ?3 R9 b7 ?
pop = [] 4 ^7 |+ G2 h& h5 R for i in range(self.parameter[3]):) L P6 j* O7 W
geneinfo = []% n/ D" n9 E+ V0 I$ \4 _+ X* m
for pos in range(len(low)): ! m. h, O+ M7 c# m o9 l/ O1 c geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation6 w5 a5 \9 l) D& T2 G
/ Q! c1 v) w$ Q0 x. c fitness = evaluate(geneinfo)#evaluate each chromosome [3 B1 S7 H) Y3 j! l: y; t
pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness ! y0 U: A; z) p. I& z7 w 3 H! |5 [3 W9 @9 E6 C* P self.pop = pop / Z4 t% q/ A% u, `2 t self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population # k g4 A+ J6 c+ @" M3 O9 P& F; I( i! Z* p6 Q& S
def selectBest(self, pop):+ x ^& W5 P* G
''' + K+ F2 Y& M9 g0 w! e) { select the best individual from pop + }! S5 X( y9 V" s B+ K- u4 x3 a '''7 Y( n5 _6 l; q+ p% u; W; ?& u# ]
s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False) O" o4 g2 F7 h9 }* o \0 F return s_inds[0]# A% k7 `2 N6 P. u: ^, v6 R: i
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def selection(self, individuals, k):% @5 K7 Z3 i. s" i
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select two individuals from pop $ M8 K! f% o( u" F, N& Y '''2 G, p+ ?0 x e: Y( i( ^
s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness $ Y! T, a$ X( U: o- N- s7 A5 K sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop8 S) N- Z+ D$ O y/ A0 S- }
# E, {/ g7 K( f5 K* J1 P chosen = [] " n+ c" [" S ]! U$ B for i in xrange(k):. E" \) c9 c5 k4 j. E) a. ~0 z
u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits]" L, T( K( U. X
sum_ = 0, V3 K8 ^1 G1 e) s
for ind in s_inds:/ A( F7 M. [+ ~% Q: I3 a
sum_ += 1/ind['fitness']#sum up the 1/fitness3 u/ L6 v9 O0 W* U6 P1 Y
if sum_ > u: 3 i2 W6 A/ \- K #when the sum of 1/fitness is bigger than u, choose the one, which means u is in the range of [sum(1,2,...,n-1),sum(1,2,...,n)] and is time to choose the one ,namely n-th individual in the pop % G ^. Y* @9 p: N* a8 B chosen.append(ind), L$ g; ?" Q9 [# Y& f+ D
break 1 ]* N) W2 v& t& m. r0 v0 O" [3 G& s* a8 _
return chosen 1 U! K2 G g7 a+ x/ \1 v) J
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: \' ?8 q9 a% S+ Y) e def crossoperate(self, offspring):8 M( r/ E- i( k8 o' V
'''! y7 @0 u) A5 _* k
cross operation # H6 c! U$ `4 l+ z, e6 r '''0 f X W/ a/ f( Q9 G
dim = len(offspring[0]['Gene'].data)) K4 O1 I/ d. R- K
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geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop 4 H5 b$ k% ^% a" f, _. j( H# t geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop ; G V) W" @% u6 n! Y8 ^ ) n E0 u: u5 {; N B% D pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, / _1 b$ X' ?/ Z; G( m. |. _ pos2 = random.randrange(1,dim) & K& u& y& I/ D2 G! K* n/ ]+ E7 m% Y I
newoff = Gene(data = [])#offspring produced by cross operation) E2 A( W' f# _, Q, k! R
temp = [], p3 `4 e% R v& I: C9 r3 b+ T
for i in range(dim):6 j7 `, Z% b _& D* h
if (i >= min(pos1,pos2) and i <= max(pos1,pos2)):' E/ u9 O! ] B4 x: }% m
temp.append(geninfo2) ; X9 L" ~" F Y7 O #the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)]) I3 Z0 C2 I! {) x3 W' Z
else: |) ~* A5 U8 W, ~3 s3 K4 b, `8 K
temp.append(geninfo1)1 R% d/ `! U J! e3 f+ b' v
#the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)]- u; I$ k- l& ~* b: A
newoff.data = temp+ \) _/ Q2 w) n1 m
# ~" b3 p+ f2 m: \& J) m* N6 U4 @ ? return newoff & Q; n3 a$ I) X+ `2 u+ I+ a 6 M, i5 W! t% l. [4 x ' |7 l% M6 U( g# i0 e def mutation(self, crossoff, bound):1 F! Q3 j% O' |+ a0 s
'''0 [7 W! U( G9 C- a, `
mutation operation1 B1 B' L9 L6 L
''' 5 @2 ~8 k: y2 o/ p8 J+ S1 n; j; I \% l) w" T6 w4 X
dim = len(crossoff.data), F: O, a/ U! t D0 l
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pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation.- K! M# @! ?; T0 h" z+ t
# u4 ]5 H/ \$ U3 Q+ P7 ? b crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos])6 S' |7 r Y* a0 v, I
return crossoff: ^1 d, [2 N( y: v/ Q2 i9 y
3 G8 {9 [* F! p/ j4 N0 ?* K7 U- x def GA_main(self): # z5 [. I, v$ H9 M" V ''' - O [7 B, f( f. @( |& U9 a. s main frame work of GA, R3 F* w0 Y( \' A1 n3 _
''' 0 O8 \3 m" L! p% h8 v+ F0 x3 j4 Y1 y1 o
popsize = self.parameter[3]& {! ~$ ], [, o4 Q, K$ X
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print("Start of evolution") 6 V8 }( h) ^' |" I- W) m$ n5 r 1 C# h0 J- M2 ^2 i9 `" S9 Z4 z* r/ y # Begin the evolution/ x7 }2 k3 @6 I `; H! W% ]
for g in range(NGEN):) e P" z0 |0 ^: g. w, `! M
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print("-- Generation %i --" % g) 8 X' a* X/ i! W
% H) Q8 w! i6 C$ p #Apply selection based on their converted fitness" R$ a6 y, f; [ C
selectpop = self.selection(self.pop, popsize) + I- C9 M% Z* \: _2 I" D7 |3 T . r) H; ~' k. r# m2 w4 I nextoff = [] 7 W. [8 _( z O. |/ @2 r% c+ M
while len(nextoff) != popsize: % X& a& W6 g$ l, |4 `3 M% ]% u. D- r4 L
# Apply crossover and mutation on the offspring - U: r. x. R/ I6 \6 ^8 T 2 ]8 U, _. D6 b' h- v6 F, { # Select two individuals - H6 t4 p+ j1 o k: }! V offspring = [random.choice(selectpop) for i in xrange(2)] 2 w' v: C( J4 q0 m' A , P# _6 `7 U Q% x if random.random() < CXPB: # cross two individuals with probability CXPB' V! v. \- d. a ?+ V
crossoff = self.crossoperate(offspring)( n* V4 ?9 i; y% H; r) G, r
fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals , n& V& n u/ n5 |7 o1 M2 {
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if random.random() < MUTPB: # mutate an individual with probability MUTPB 7 X% T: d( [8 x; K% ^) s muteoff = self.mutation(crossoff,self.bound) 2 m# ]3 y/ c+ F) H fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals- f0 ~$ H, v; @ M8 m4 j% F9 E
nextoff.append({'Gene':muteoff,'fitness':fit_muteoff})1 t" i6 u6 { w3 k* |
/ x7 S) b# [, D" h" h+ b: J& [1 Z1 @ # The population is entirely replaced by the offspring; ^ H% t3 X5 J) {
self.pop = nextoff( ?$ I2 R2 s- |: `7 S7 R
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# Gather all the fitnesses in one list and print the stats" r; Q4 e, [4 E7 u; K
fits = [ind['fitness'] for ind in self.pop] ) v- j% a k% ?% q5 w2 V% D- M( N( T T) ^8 H
length = len(self.pop) % Y1 `- U( m4 I- g mean = sum(fits) / length , C( v; V4 V( l8 }- @3 i, a sum2 = sum(x*x for x in fits) , [+ Y9 h/ D5 n std = abs(sum2 / length - mean**2)**0.5 . `7 Q& h; F6 T% J% f, l& U best_ind = self.selectBest(self.pop) - H/ @ e7 I& J ]5 _$ Q/ h: [3 r 1 S D0 h9 f! h- p* p9 F if best_ind['fitness'] < self.bestindividual['fitness']: l- J* _+ _3 G$ o% q self.bestindividual = best_ind7 v6 O0 ^( h" q9 ]. c2 C5 y
! W1 I+ O7 n1 `3 t3 N$ v2 V7 E print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness'])) # G1 f+ `/ R3 L+ G print(" Min fitness of current pop: %s" % min(fits))7 L* O, E9 F3 g, w/ y8 e
print(" Max fitness of current pop: %s" % max(fits))9 s: e/ H$ [% \* K6 W
print(" Avg fitness of current pop: %s" % mean)" y7 u) Y' F, U6 M4 \+ P0 h& `2 D
print(" Std of currrent pop: %s" % std)- v) q6 z r' o/ h! F: }6 W
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print("-- End of (successful) evolution --") 7 F& ?% T: g) q3 A" V$ Q" t: W' b5 m
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if __name__ == "__main__":; {' i2 D0 ]$ L; v) k
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CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters 4 E* ?# P5 Z x9 | % G9 }- c+ ~- d# J; l' i# q up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables( I0 h( U a5 J" @! K
low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables& n" P* L, ]/ A# p! S' m
parameter = [CXPB, MUTPB, NGEN, popsize, low, up]- T2 B) j0 S E
7 R/ P0 M# t* A& E1 Y8 P2 n: A9 ^ run = GA(parameter)& F$ e/ l1 y* a3 n4 j* i4 }
run.GA_main() " f. ], s1 M! |' F1 T———————————————— + l, H; ]" x$ x版权声明:本文为CSDN博主「bible_reader」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。( F* A1 \( g3 p9 r* d5 ^; P
原文链接:https://blog.csdn.net/bible_reader/article/details/72782675 8 i& E; o8 [8 E1 j e& o N" {8 Q+ W. G 3 z0 I" z$ `' c/ a& s X) p