# J$ G- `9 m7 Y9 A) {3 F4、Python代码 9 r6 j, \6 {5 ^#-*- coding:utf-8 -*- & [# S. ^+ ?& [: w Z9 d! L. z" R8 J4 `
import random ) w z, s1 V; F( K: i; uimport math* |4 p" o2 [( t! E
from operator import itemgetter- y6 X3 I, A4 R6 B8 K
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class Gene:: `& h- R4 A: P2 W; ^4 a6 e
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This is a class to represent individual(Gene) in GA algorithom , ?9 B! ]; r8 O; X- q1 L! } each object of this class have two attribute: data, size " ~% m5 z6 _- \% c+ H: [& E ''' : p5 q4 q8 |" |; ~8 q1 ? def __init__(self,**data): ' A* I7 ^5 @$ m h0 P6 F6 X self.__dict__.update(data) . {$ A* G$ \1 Y6 m9 ?- p: Y
self.size = len(data['data'])#length of gene; P$ N; ]1 v9 P; `
, \: s9 d$ l- G3 B* s 4 h1 o5 u" ~7 v" d$ Mclass GA:7 e3 b* ^/ U, t8 `0 p. ~2 k/ o, }
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This is a class of GA algorithm. - h4 }" g5 r8 u8 u* G4 V$ E; B '''$ f) X7 i1 ~+ E
def __init__(self,parameter): 3 h( U9 D, i: E( } '''8 ~+ K4 ] s+ m. }0 R6 z0 z
Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value . a7 C& d6 E' N The data structure of pop is composed of several individuals which has the form like that: 8 v4 q" _* z/ l Q. M* D W" E # Q; b9 j. u# s/ _8 b" f4 q {'Gene':a object of class Gene, 'fitness': 1.02(for example)} K0 d: N; V; |: h9 ~/ U3 N Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2] # D) E$ ~' n4 I$ a1 c3 p) \3 i n* O8 `& W
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#parameter = [CXPB, MUTPB, NGEN, popsize, low, up] $ T d% Y3 i3 s, g: _ self.parameter = parameter: k8 g( X% V$ P& n
5 H6 A% h g3 n6 W" r* F low = self.parameter[4]+ |) \4 I, H; t! Y9 s/ h
up = self.parameter[5] 2 _8 B% j, f2 c1 u, k8 f: u3 M , f6 }$ A( [; a( D) U3 e" b' C self.bound = [] ( M! Q- ^; x( O B self.bound.append(low)6 b5 t, y }/ J" B
self.bound.append(up)/ ~( a. _+ x V& u5 o+ I
2 O2 ^" \& J9 N* r7 A- n pop = [] 3 k: g( i7 [- }% O/ d for i in range(self.parameter[3]):. i7 v: z5 a! x4 z8 \
geneinfo = []/ b# {. |% N3 a* }* x
for pos in range(len(low)): 4 g1 B& L' h, n5 B/ _0 J; W" h geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation / W& O) ~- G( I4 S; ]/ g. }$ B% {4 ~/ ~* W+ Y, K) z
fitness = evaluate(geneinfo)#evaluate each chromosome4 U8 o: |9 }3 G( b$ w
pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness m) R4 Y L& b, r 5 s/ Z) Y% U% V1 {2 H self.pop = pop 1 i& B- ~: O4 L$ H! M! z4 l" q& T self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population+ {! F+ z7 S/ P& c$ J# M
$ D5 Z4 E' E$ d def selectBest(self, pop):. \) M! w* r& l; ?: o! w2 d2 i. d
'''# J3 u+ U# f( O6 G
select the best individual from pop 8 r7 r* W7 M* O4 z- A ''' 7 u5 W1 Q) e5 r/ _0 U2 \ s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False) 3 }9 E2 [$ u8 v return s_inds[0]. T( |8 U: M$ L& h8 m0 w6 v9 j
5 i9 y9 Y" d7 K3 A2 i& f$ v& \: H def selection(self, individuals, k): 2 B; R5 o$ H2 U3 U' p# H" u3 h8 J* d ''' / L. D( G: O2 N3 s* P select two individuals from pop 3 v$ p" M: J- T0 `" U; ] '''' w+ a4 ]( I+ C( ^
s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness * p2 [; G2 o- C* {3 O0 [: S+ Y
sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop 0 s# C4 ?, O! T. @4 P* M1 u / v. D3 U0 D- m chosen = []/ W4 c) _) p( Y& W
for i in xrange(k):# W, i8 r! s7 r% B _2 \8 X, U8 ^
u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits] 6 E: Z4 ?( T' n# |: u6 [ sum_ = 05 x; p) U+ V" D6 O) D9 H* y
for ind in s_inds:: b& u0 D$ g; L: U5 l' E
sum_ += 1/ind['fitness']#sum up the 1/fitness h' s/ I4 M( [; U) [8 ?6 T- L U
if sum_ > u:( W: x, `! [$ F, L8 N# T) R
#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 pop6 B- j- i& m8 X; U0 L- U2 L4 H2 D
chosen.append(ind). }; \+ d8 t% |" q# L1 y5 Y
break4 I& a F( O* }# q, l
3 B$ u( _3 V8 i G1 e return chosen . p/ b- b# p( ^% g$ L
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def crossoperate(self, offspring): $ J1 G) m$ K5 X% r$ x ''' 8 a. H) V- d) s+ b ]- x cross operation ) A/ I* p* X' E8 ?9 R+ z. G. K '''( r: e4 e( E6 ?
dim = len(offspring[0]['Gene'].data) : Q- r4 C9 J4 n6 D& O) E& Z* y6 O: U1 v$ m- H" j
geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop |2 d1 X8 O, L/ e. ]) |% M
geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop! f- D% h' e- e
+ l4 r: `8 p' U3 [" z) u; Z8 W pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, 7 N: D5 \8 R' T3 G# Q pos2 = random.randrange(1,dim)% B1 e* c9 X9 N% k+ U9 g
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newoff = Gene(data = [])#offspring produced by cross operation; F- p' M5 e2 B
temp = []1 R( v2 k+ C2 t* G2 S% c7 ?
for i in range(dim): : e! b- f* {9 q8 U/ H( z if (i >= min(pos1,pos2) and i <= max(pos1,pos2)): 5 q* V) C3 g3 w temp.append(geninfo2). K' ] P0 }4 Q$ p6 Z: u! k# o
#the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)] 6 t# ]; P+ Z$ E5 r6 B# }* b else: ' c P0 E4 j5 v3 \! {4 t- h; s temp.append(geninfo1) " \7 {' Q3 p" a# @ #the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)] ) s. d" K0 y& S newoff.data = temp ( ]$ w3 K' ^7 C8 e) P! W0 `& _. G6 H7 a# d, E) u
return newoff % w* n e8 |3 U+ ]- H: `$ s J5 M0 h/ U9 t; b9 l" S ' L) d' H# M# L- G' Q( P W3 k def mutation(self, crossoff, bound): + m8 v. M1 I5 w9 c '''# U8 x% V. b5 v9 X5 b6 ^
mutation operation % ] _+ b) h! m$ z {4 n# Z ''' + }/ p9 o; o& l1 ^. ?* B" f 5 f/ T; A4 H0 L) x5 Q dim = len(crossoff.data)7 |$ l- b! P* `4 W2 f w6 T }
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pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation. 0 c, u; E* g6 N, x R5 E 6 w' D6 ?, n: T; o: |. E crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos]) 7 G2 T% k2 d, M return crossoff/ _4 `2 y+ y q" z2 R ]
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def GA_main(self):3 |" y P3 w- S7 q
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main frame work of GA; m6 ?5 g' r) V f7 K& l' \
''' 6 B% V$ w% g D" U ' @1 ` @" W: j' T8 ]& X popsize = self.parameter[3] - \) X" k8 a0 T+ D$ Z3 H, z* \/ t! I/ U/ D3 ~4 M4 t
print("Start of evolution")* G2 j+ R8 s( Z$ g
1 i! O/ _# F# y # Begin the evolution c0 S" s2 w, O, a for g in range(NGEN):* ~% J; M) B( m0 b% t
& v3 O% d$ \' p7 M) A W print("-- Generation %i --" % g) & O5 j) h' G* c2 `, y
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#Apply selection based on their converted fitness0 g! D6 s$ W, `
selectpop = self.selection(self.pop, popsize) & n/ D3 N `5 R 5 U; Y8 i$ b( @2 g7 s1 [* ? nextoff = [] ! p4 O+ G z! q while len(nextoff) != popsize: ' g( Q& p5 |: i
# Apply crossover and mutation on the offspring , A+ P* ^7 C) L1 x. c5 H
9 T3 b' a( i; R k$ W # Select two individuals 9 C, [2 w0 d$ n2 }8 { offspring = [random.choice(selectpop) for i in xrange(2)]" G- _/ @- @8 d4 s+ [
' `' i0 g" T7 t8 h; c* [- w3 x+ k& ~ if random.random() < CXPB: # cross two individuals with probability CXPB; S, E8 D: _5 f* g' W
crossoff = self.crossoperate(offspring)4 ?. e. p" ]& l, M6 p
fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals 3 ?+ l" A/ ^, [& y9 x
. N$ N$ p- Y9 x6 w; X7 j, p1 F if random.random() < MUTPB: # mutate an individual with probability MUTPB7 Q) q! n6 u" j) n" |, w
muteoff = self.mutation(crossoff,self.bound)" ?& l) s |; o' g# _
fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals% g5 a* m e/ x9 e g( {
nextoff.append({'Gene':muteoff,'fitness':fit_muteoff}) 9 _% G. ?1 L; i" F" _# \+ m9 A! u5 y( y: W/ Q, @- G' n
# The population is entirely replaced by the offspring3 K& H! |- C' N$ l# X
self.pop = nextoff! |) J r5 y l* V$ _2 R
7 G8 ]8 q0 l2 F # Gather all the fitnesses in one list and print the stats ' J v. S$ D0 x; F' O! j6 I% k fits = [ind['fitness'] for ind in self.pop] ' G" o5 v2 F: m5 |' C) F * W! \2 L; `) j% ?) G! \ length = len(self.pop) ' v" L5 u* b: s mean = sum(fits) / length ) J% j- m. I4 j. k sum2 = sum(x*x for x in fits) 7 d" d& W$ b& ? std = abs(sum2 / length - mean**2)**0.5 1 n/ d9 K* I' c L$ r+ y best_ind = self.selectBest(self.pop) ; c! d8 K. D" Q) E# `. g; }4 }5 m: n
if best_ind['fitness'] < self.bestindividual['fitness']:0 \" ~8 p4 O$ j. C! @: X3 c: P
self.bestindividual = best_ind & l$ R/ `2 |7 s, d6 P : @" Q1 f5 \0 \) o7 ? print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness'])) $ }& u. K- P( F2 b: T6 P4 l print(" Min fitness of current pop: %s" % min(fits)) : c, o: J" M. f# Y: d, e print(" Max fitness of current pop: %s" % max(fits))- H3 U; C% I+ F' j6 E! a
print(" Avg fitness of current pop: %s" % mean) 0 g( V# I" j3 `7 [; W, z- y print(" Std of currrent pop: %s" % std), Q; P: L ]9 i9 t7 W1 Q! h0 c2 g3 T
( p& r! K+ d$ w( W7 x- |0 z print("-- End of (successful) evolution --") 9 @: X# Y8 F+ q4 m 4 u7 l3 H, X A& ?* O! Gif __name__ == "__main__":, S6 x, y4 T9 C) ]! R+ E
- U$ b2 ~. R N. |- d% `, J CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters & I( F3 h+ Q+ e2 v. W$ b' u: K7 O; \7 o: \) W
up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables % `/ h8 r: k7 j% B2 M: r! H, \+ x low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables ) \( B% U. V+ p4 y7 d parameter = [CXPB, MUTPB, NGEN, popsize, low, up] / [4 O5 D+ i }$ {, \0 j7 |0 ?4 D& ~* Y
run = GA(parameter) 5 o: m. F7 T N* a4 [# @, } run.GA_main()# u6 q/ W7 h$ R* h, L+ y& i
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原文链接:https://blog.csdn.net/bible_reader/article/details/72782675( p8 @5 w7 C% \8 P" I- S
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