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4、Python代码3 J0 W$ t6 p7 [" y' z
#-*- coding:utf-8 -*-% R. s. [& l$ ^ G, I, {) q% @
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import random ! }& Z( W) @1 }7 U) \$ Pimport math+ t A$ ?3 S h- I
from operator import itemgetter 8 Y' f8 f! Z" _* q4 H1 U/ @9 V8 c
class Gene: T. M1 u4 w2 J9 m
'''2 `4 r% i3 x/ F0 B4 L: Q
This is a class to represent individual(Gene) in GA algorithom4 c& m7 a- M3 Q( ~( ]7 q7 _% Y7 K
each object of this class have two attribute: data, size * n6 s, j2 D8 s1 i$ |: S9 ]" q1 m '''5 }5 l. h. w) ]
def __init__(self,**data): 3 y% n: @! B G6 j6 y self.__dict__.update(data) ) i7 F c, m6 {7 C, E2 G; O3 y. n2 J self.size = len(data['data'])#length of gene + z. w! M; z' n$ D ; t. z2 s) Y; `. M' @3 C3 Z9 Z# X* ~% q
class GA: " H7 O2 |$ g* b' B& T ''' ; W: B# K" Z' I( W- p This is a class of GA algorithm. ' w6 A+ X5 x3 x* {) J1 R( S ''' 2 x& X1 `6 J9 T; n1 O def __init__(self,parameter):8 ?" `; g1 R$ U; l- E
'''% l0 i; S/ I( I9 |8 n7 f/ O# m+ }
Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value ., c- {) s5 O# r- N0 f% F
The data structure of pop is composed of several individuals which has the form like that:6 a; U7 T3 Y3 P( {% n
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{'Gene':a object of class Gene, 'fitness': 1.02(for example)}0 B" w$ L& r. c( v2 H. t0 e
Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2] 1 s4 D- @$ }. i; m4 A/ l- w - j s+ G& T9 J3 H# c '''9 A8 \! G+ s' X1 d# C0 S- \* n, i
#parameter = [CXPB, MUTPB, NGEN, popsize, low, up] " A6 Z1 D+ x1 A& f self.parameter = parameter ) y/ z( V1 o8 s Q# n; a" r6 v
low = self.parameter[4]( V6 o( X; s- ], Y
up = self.parameter[5]6 B) A% `, h8 ]2 z0 Y- G; s# t& X
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self.bound = [] 5 e- }# S' V1 V5 a1 s self.bound.append(low) , e8 m' K6 z2 V self.bound.append(up) ) d& K( h7 y0 \$ z ; S. y ^, f! m2 D" \ pop = [] % `3 u) ~: ^* l, X1 V for i in range(self.parameter[3]): 0 m1 p! ]/ q4 s9 a5 L) {+ @) p geneinfo = []( f8 w0 `4 g& W4 U9 z
for pos in range(len(low)):$ `! k7 ^1 T$ [) b
geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation + z- m: ^: E. _. q4 Q/ J, a, P 0 {1 \) N$ ~: R fitness = evaluate(geneinfo)#evaluate each chromosome( e6 A% m1 w6 f1 d X# M; q& [
pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness9 c: e3 S0 {3 g" {8 z: C! m
+ C/ i/ H# i- h3 o7 j# i self.pop = pop 4 w1 d* g2 i. ]' \2 t self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population& f' N( _! [9 j$ y% Q
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def selectBest(self, pop):' k1 g7 E8 n% d
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select the best individual from pop9 i, F* L7 r1 [* M" p
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s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False)! C' y) f; K8 D" I' R
return s_inds[0] + r1 t" N4 u. k/ r x! u; t4 ?0 ^. I' ]5 [ def selection(self, individuals, k): ! |; p+ h; l" h '''7 k" Z3 m( p3 W* s. e. ?
select two individuals from pop! {1 ^: g9 k+ a$ J2 d" ~" y
''' * | s9 R$ P w% U& ?( M4 U& @ s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness % `+ t6 a4 ?( x
sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop" u% b7 I* h: t1 }7 [
1 J3 C8 v: h8 e7 D6 I- h% J. H chosen = [] $ h3 [; l. d, X- Q$ [ for i in xrange(k):: r0 E( h' U1 b% B
u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits] 2 F( Q. ]! U8 Q0 M sum_ = 00 G! s3 l3 r- [0 Y; L% n
for ind in s_inds:1 r7 S1 n d8 `9 |) [
sum_ += 1/ind['fitness']#sum up the 1/fitness/ H% R' ^8 s+ _8 [6 e- g! |5 d
if sum_ > u: 9 i) ^& ?" u- c% t1 e5 B6 ? #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 2 G. q2 o( Q% A. O& ~ chosen.append(ind) i, D k. c. N break 6 Z! m2 ^) S; q. \) d + ?$ Q) K& C1 n return chosen : c! i( A! q# o. p8 C3 L8 ^
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# J5 E/ \7 [5 v# T( \ geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop 0 |- W8 H& O1 | S+ l. l geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop7 |% c/ U9 r' Y. ?/ E$ Y+ \
3 d; b0 j8 n3 \ pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, % g8 m# n( H; m9 g- y) j
pos2 = random.randrange(1,dim) " Z- T' a7 Z6 R 4 m5 B# f) c7 F& G newoff = Gene(data = [])#offspring produced by cross operation / Z! h; `# A6 |: n6 }2 X temp = []) @& x- z, e% \$ E+ B/ N
for i in range(dim):9 F0 {; K8 C; ~& O2 a: a5 H
if (i >= min(pos1,pos2) and i <= max(pos1,pos2)):* w/ O0 u* D9 T5 G4 @" e( F% ?
temp.append(geninfo2)6 A6 c/ I ]8 c8 e
#the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)], Z3 t) b0 G* R. F b5 X8 u% a
else:( A+ j8 o- z! c0 d6 O! o% e
temp.append(geninfo1) 4 q* W7 b1 {2 t7 v7 e* F o #the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)]. [# L7 S) h0 ~3 m8 ^2 w/ `" ?
newoff.data = temp ; x4 A) l+ w- e& k+ V/ D7 Y- V* J- _8 r( t
return newoff" Z: o2 l- ~( f% {" f1 `
; w( m" g5 `, ~& } dim = len(crossoff.data) 3 S* K( m9 u ?+ G4 h ' M6 c1 ^8 K5 J- b9 _ pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation. 2 x1 V2 \/ c* _# U! y$ ^ / o* `4 {7 V8 p- ^- z. \7 t crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos]) 9 o6 n! a ~3 a# e9 g% _ return crossoff& ^7 i: Y& o" w! A2 g q/ R
/ T# {3 L* x0 e1 u def GA_main(self): " ]+ {; \* `! p3 v- W5 g' l& R/ j '''6 D4 V- b. e/ m4 b$ r3 @& }
main frame work of GA : \' |8 R" x& L* W# m. t3 } ''' / V3 O) D& Q/ ]9 |9 b6 H 8 H3 M* G V. K popsize = self.parameter[3] F- |, y$ M. X/ n y7 s
" y3 _" e* }* q, E print("Start of evolution")+ t, ^* k( C- _' t
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# Begin the evolution & i6 b. }& t" z1 F1 U$ R. H5 G3 P for g in range(NGEN):6 N3 o5 U) A7 l/ `0 r& B
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print("-- Generation %i --" % g) 5 k0 k% h2 n" o2 H; n7 g# B
1 D- g+ x9 e! p+ G5 z #Apply selection based on their converted fitness9 F& V0 w- n" Q
selectpop = self.selection(self.pop, popsize) ' o8 x% \( s' i
" Z/ q4 K( e* A, i7 h$ s3 o4 ` nextoff = [] & _0 @) L! r5 x( t/ D
while len(nextoff) != popsize: 0 x1 P* Q! {' L' B9 [ # Apply crossover and mutation on the offspring % \- W% z' {' p3 O M
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# Select two individuals ' i# o b E/ K% g9 f offspring = [random.choice(selectpop) for i in xrange(2)] * R* b* C7 O8 a# l) y- D \7 w! S# w9 e. s& {# \ if random.random() < CXPB: # cross two individuals with probability CXPB 1 s! e5 {; [% P# ? crossoff = self.crossoperate(offspring) ) b* j/ H% A8 c; b fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals % @ P6 C+ H1 j( b4 R B. U1 }& Y! n% h7 H5 g! F# H
if random.random() < MUTPB: # mutate an individual with probability MUTPB$ V6 m0 X( [: Q
muteoff = self.mutation(crossoff,self.bound)% d3 ]* j& u. ^
fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals* V/ Y6 x( Y4 t
nextoff.append({'Gene':muteoff,'fitness':fit_muteoff}) $ P/ d; x$ I0 W( R& Q. u {" N/ M; `5 t% T/ J4 w) G7 V+ x; g
# The population is entirely replaced by the offspring: h/ x1 ~, x( S" f) ^% s
self.pop = nextoff) T) E. B) c- I0 e5 z$ g
3 C4 p8 D, C1 P" L& ^5 K # Gather all the fitnesses in one list and print the stats / T2 i L. ^. O y fits = [ind['fitness'] for ind in self.pop]/ R5 ?2 w* J( a; {8 F% P
$ }. a; U# |6 X9 ] length = len(self.pop) : B: y6 {; v) o; b, J0 P$ g mean = sum(fits) / length- x. s, p3 [& ?# x' {# A- X8 g
sum2 = sum(x*x for x in fits) Y+ N9 ~( g& c( K% f0 I3 p std = abs(sum2 / length - mean**2)**0.5- M# t6 Z( v7 X: K' i3 i; r
best_ind = self.selectBest(self.pop) % L" X6 A8 r3 K# l D. c , \4 p" @8 {% a2 ^" o if best_ind['fitness'] < self.bestindividual['fitness']:$ h* Z) A+ D0 G+ m" J/ `
self.bestindividual = best_ind 0 Z# y5 I" T$ G p( s ; m- Q5 E. Q9 X print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness'])) 2 ~, s& J( b$ \" z6 T8 L print(" Min fitness of current pop: %s" % min(fits)) 5 ?% o8 }6 h' J( y4 F- ` print(" Max fitness of current pop: %s" % max(fits)) 6 c5 @! u0 @% p T& Z6 C4 n" t print(" Avg fitness of current pop: %s" % mean) ( A5 l! ^" | M/ e D print(" Std of currrent pop: %s" % std)9 H3 [$ v8 V7 g
0 Z! I' C2 X1 \9 S print("-- End of (successful) evolution --") ! e; O: g9 v9 Z- o! _' r& s
' U. D: j9 X# y4 o! J1 sif __name__ == "__main__": 1 |0 ?8 g- p5 Z8 }9 H6 N , ?- N. T2 k3 b$ p X) E" ] CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters ( A4 h% v b) z' s2 V8 t/ M* u* Y0 U
up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables- [9 C6 o. X5 `6 Y t/ w" \
low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables& z% l, H( M# y" b& w9 ^
parameter = [CXPB, MUTPB, NGEN, popsize, low, up] - M1 E& \9 Q! \ + ]; i9 L) \' z+ K( B/ w8 u run = GA(parameter) 6 ]! K! b Z3 t, x+ v9 @ run.GA_main()8 Q+ r9 w0 P, J! Q
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原文链接:https://blog.csdn.net/bible_reader/article/details/72782675 . j/ m' m5 Z$ a! {4 I5 w1 t : ~; ]* u8 m& G+ ?, L4 o5 R8 ? s |3 n2 f$ w8 r- F* H6 _. b1 A