9 q( }' l# Y1 E( j- j! T1 w4 y4、Python代码 7 S! B4 `. H- `7 z( P' t, I#-*- coding:utf-8 -*- 4 [3 o, w2 I% v/ |, m7 |! q( Z& Z6 ?0 s2 E: h
import random $ j. c& K o2 ?) b: Y! ~# M: [import math( \. d: p5 o+ ?& s' `9 P
from operator import itemgetter & r" ^* _; A& Z0 M% E y# s* S8 W! u1 j$ n6 F4 m! L* z
class Gene:# ^4 g) _; a; N: L2 s$ R5 y* x; _
''' ( L9 L ]0 E# f6 G This is a class to represent individual(Gene) in GA algorithom3 e7 x& ?& j2 ? O8 d$ [
each object of this class have two attribute: data, size ( @+ d0 K- _# B7 [8 N" g ''' 2 Y6 m! l% M7 s2 ^% S c def __init__(self,**data): : x. r0 `2 c& v- k3 X self.__dict__.update(data) ; @2 |8 v. d0 m I9 v- t
self.size = len(data['data'])#length of gene% h- c* R! U5 m% `8 Z; }0 r
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% b9 d( W" l1 p3 S3 u8 i# Xclass GA: , A3 V1 h) [$ o" C, V0 w* W '''- r6 J3 c* e& s% `; _
This is a class of GA algorithm. 3 ]% C" p5 p/ [5 t+ @1 S" M
''' # G) ?4 x. S7 q6 W% h) s7 D def __init__(self,parameter): * A7 o( {" g2 } t& L# k+ ? ''' + A+ k! Y& b0 Q2 {3 C Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value . ' x& ?, y% Z1 F1 Z The data structure of pop is composed of several individuals which has the form like that: $ v9 r: e- ~. U1 i. c- r1 R3 k% {& I0 o' d
{'Gene':a object of class Gene, 'fitness': 1.02(for example)}) Q5 S3 m: _( F) e* B" j
Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2], S( |( {4 i0 X5 N1 d
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'''5 N, e, I I" i5 @: x
#parameter = [CXPB, MUTPB, NGEN, popsize, low, up] # z3 Y" H2 b/ o7 p9 n self.parameter = parameter1 `! o6 u& Y9 n" G
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low = self.parameter[4] % m0 p3 P) B) Z* a( o up = self.parameter[5]5 c" x, g F* ^5 J% P" I6 x
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self.bound = [] 9 x. y, N" Q+ }$ S7 l self.bound.append(low) 0 E Y7 m( P; A* C2 k. c self.bound.append(up) - M8 v9 _: z! S* ?& v) _, {( g# e' C* Z
pop = [] ! T; p7 ?$ \& x# N, W( e$ v$ c for i in range(self.parameter[3]): , C4 F. G/ N) Y& v# L% m3 s: g geneinfo = [] . x3 C9 \- g( v; s3 b* w; y for pos in range(len(low)):+ P- Q* o, C2 i. y( B" w
geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation, |6 o; V% J$ T9 N' [
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fitness = evaluate(geneinfo)#evaluate each chromosome; f4 p- ?( i4 Y4 u0 c
pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness : L% G6 p1 l6 l4 G H& @" W! b7 G9 ` W4 w6 m. Y
self.pop = pop. u1 Q x* ?- c2 Y6 z5 L8 H
self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population3 s+ t. Z2 P7 n7 M/ U( k
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def selectBest(self, pop): % h( c) y% y7 f) z0 V$ m1 R ''' + S" O5 v. r* J" u: x select the best individual from pop " m8 G/ _0 S; G0 L$ o '''7 }( o* k+ p, f, m
s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False)3 P% K8 z+ |. @/ Y
return s_inds[0]7 {0 V1 }- @- j& ]
4 ~3 {4 d, M3 @9 |* g* d* B def selection(self, individuals, k): 8 y/ h4 r. k. E1 e0 y/ \4 E '''. R! u& n. F2 k2 i: N/ e/ B. Z
select two individuals from pop) A: V, P+ z' n6 V0 a
''' " Z+ p! o0 G; [0 L: t' |1 n s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness ( T( a0 @* Q4 p! h" y, M* K( q; n
sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop& \; t( s j) J( X6 l* X$ U/ N+ ~" x
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chosen = [] ) P7 w) K% c4 }' f for i in xrange(k): ) Y9 ^ R1 w+ J8 U* U8 D u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits]1 n7 w! Y/ ^* {: M8 d4 c. r4 g
sum_ = 0 5 o$ ^ c- d" u/ L$ p for ind in s_inds:% L' L J+ C1 ]6 T+ q
sum_ += 1/ind['fitness']#sum up the 1/fitness$ W: ~# c; [. O7 I# d
if sum_ > u: 5 B% z' p0 B5 l( y) f #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$ r% `0 n4 T& y# D5 L
chosen.append(ind) : M6 s5 m$ g# M; C/ R7 Z8 q break / W5 ]( b5 b- e% i | _! U 3 u0 l7 w0 |7 u, r* t& V return chosen V. m1 ?% \" i: b& F; t' X. a
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def crossoperate(self, offspring):& b. h0 z5 Y$ f: L2 K$ i+ `
'''! d* J* g7 J7 h; I0 Q/ \! G
cross operation 0 A: _0 D% q3 A8 n% i! J '''2 }3 L2 f0 P9 M$ l$ v6 \* |3 I, H
dim = len(offspring[0]['Gene'].data) 1 p9 M' }+ k6 G# D2 F1 T! D M2 I( s6 m
geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop : P* n" E; {7 ~! ?0 m" t geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop / [8 s! v# R* Y2 K- {. k' i% A) i( i 9 `% z, P# S9 T! T pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, 6 v6 Q+ V ~8 Z pos2 = random.randrange(1,dim)" s' M/ L$ B, r' T: Z8 `
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newoff = Gene(data = [])#offspring produced by cross operation 6 z: }1 Z& N) @- t temp = []! ?" j2 A9 D+ ]7 P7 b6 v6 b$ d
for i in range(dim):# [' W2 S, ~- ^6 ~
if (i >= min(pos1,pos2) and i <= max(pos1,pos2)): 9 L# `3 H; t C1 R* W" a( e temp.append(geninfo2) / n* G5 k3 P+ P7 X) u/ c1 e #the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)]( ~& Z2 z# m# h" ~( Z; v
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temp.append(geninfo1); m3 u( b3 a8 T* \5 W
#the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)] % C( H/ d# M4 z( L3 b' y/ w9 z newoff.data = temp& s: e; f* Z! @( Q" ]& l" z+ q2 i/ P1 k
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return newoff1 L1 G0 i9 P9 H# ]8 H
$ W( T @6 q3 v" n: E 3 b$ a. E# k7 Y1 S: d def mutation(self, crossoff, bound):. a( Y$ P/ V D
'''9 C8 B+ b: d% X% W- Z
mutation operation' H0 D' k/ v: I% ^0 L
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dim = len(crossoff.data) 3 b- Z' ]- E# H; z+ c4 {; r- @
pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation.8 m; U3 P8 [ M5 ?+ ?+ Y
Z/ u A: K+ M1 B* g crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos])! ]+ l8 m: v$ [. {4 L
return crossoff % x& {3 Z! J5 {! J! w$ P+ G( ]& x, L0 W( D) |
def GA_main(self): Y5 K2 K- o; t& Q% s+ j6 {, u ''' 2 |/ R, r* c) P4 j0 A* \# n main frame work of GA , E9 V. `. t4 p '''; l( p+ L( c2 i4 f5 a& X; w4 o6 N
; d5 k+ y8 A+ W6 i popsize = self.parameter[3], D- M* X& F' Z* C. ?; K
6 _! w/ ^4 Q3 v: I # Begin the evolution / l) R3 X8 N: i* i" U' }. v. L for g in range(NGEN):( r Z8 o: w7 [5 n6 B! g6 c3 a
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print("-- Generation %i --" % g) 4 o4 @; {( Z h, l) S
8 T' s; X- X* @4 @ #Apply selection based on their converted fitness9 q# t) C8 x# o8 |( j$ J7 n
selectpop = self.selection(self.pop, popsize) ) k& b" Q h' C0 y7 u, n6 m4 V0 J+ t* ^- J F- A; M- }8 ]& C/ k# T
nextoff = [] * e$ n0 e5 D- z9 f/ Y# `, u; L
while len(nextoff) != popsize: ! ~/ C R @7 w4 b* Z7 ~0 C* @
# Apply crossover and mutation on the offspring 5 ^4 f& j2 @/ |
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# Select two individuals: ^- r5 s1 g4 W& _/ J
offspring = [random.choice(selectpop) for i in xrange(2)] $ J7 Q5 t. d; i: h# @$ p' J 7 w! q% x% B* T. o$ L6 W7 K if random.random() < CXPB: # cross two individuals with probability CXPB b, o* q3 N8 e9 q' v0 \* D, }
crossoff = self.crossoperate(offspring)0 b7 Q w) @/ W0 V
fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals 2 d6 b! U- y( M; W K K5 B3 x
0 t9 W: d8 C+ G- C if random.random() < MUTPB: # mutate an individual with probability MUTPB, |" O; x/ F, j* q8 c# v
muteoff = self.mutation(crossoff,self.bound) ; e8 E/ Y7 z( S6 D$ ^8 O fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals " V& B: c9 F3 W9 Z' j- {. T nextoff.append({'Gene':muteoff,'fitness':fit_muteoff}) 4 T% I# \2 i, i' `) C 1 j7 J, u1 W. |& z9 q # The population is entirely replaced by the offspring( b& L0 P) z% ]3 ?
self.pop = nextoff4 O) M6 ?' a2 z: K9 v9 @1 d, j' `( ?
* \. X/ `5 l/ v0 a1 x) `: J # Gather all the fitnesses in one list and print the stats # W! Z! @% x, } fits = [ind['fitness'] for ind in self.pop] 9 C' F; ?9 Y5 w0 ^& _: L8 v ) J2 u+ h5 s; m% ]4 O, @/ w3 f length = len(self.pop) * D" {& E& T4 z mean = sum(fits) / length, H: L/ i* j0 p3 r# U- k
sum2 = sum(x*x for x in fits)% ^1 p, H: `: `( I& W* u
std = abs(sum2 / length - mean**2)**0.5& P2 V3 c- _5 l, i4 }
best_ind = self.selectBest(self.pop)% D6 r* {9 g! p
4 t ^5 C* ? P- r# }# ~0 ^0 P2 \ if best_ind['fitness'] < self.bestindividual['fitness']:6 R% I6 Q4 b- d: T
self.bestindividual = best_ind1 K& X7 e8 s/ R' z+ c1 B: Z
A% {1 t; F, w+ o! f print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness']))* f" u) a0 C" s) n! ]' z( j
print(" Min fitness of current pop: %s" % min(fits)), V8 g+ f; J6 B# q
print(" Max fitness of current pop: %s" % max(fits))$ {4 O* I' J* P$ @8 u, p
print(" Avg fitness of current pop: %s" % mean)5 @: p$ V6 E T" ?
print(" Std of currrent pop: %s" % std) 2 _5 @/ U; R+ Y8 U a9 I! I+ I/ \ & ~$ J* h: t( Y print("-- End of (successful) evolution --") , w. a6 L& n* y( ~& |
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if __name__ == "__main__":, @* m, }9 J* x+ m
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CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters$ l M! P+ j, P( P; {0 k3 V5 e
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up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables* Z+ {& w3 u( Q3 \ x, W- v" k7 C' c
low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables , \0 ]: I+ I# i- W6 I2 q+ d parameter = [CXPB, MUTPB, NGEN, popsize, low, up]7 e# ] h( e, F$ s8 ^
, L j6 P2 r: S! p5 K run = GA(parameter), y, O8 R1 B6 _( Y3 f8 w2 O
run.GA_main() 0 m" s2 R: |* a# I- k/ I———————————————— 1 Z5 e. X/ X- q. @' _/ w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。 $ w/ [% s) p8 H9 ^原文链接:https://blog.csdn.net/bible_reader/article/details/727826757 U1 Y. L# X6 ~7 K% l! }6 S* N