! ?- v2 @4 R& Z+ o) f7 x4 G4、Python代码 7 S; ^5 U1 Y* s: ~* l#-*- coding:utf-8 -*- : [; @* e: P1 e( u: L7 r+ y: W) P 3 s# A1 V3 e1 A4 D) Vimport random . A& P) e7 g4 ^* T- Eimport math 9 ~' H; i2 v2 ^' j( i* ?from operator import itemgetter ' _) k( E0 I& q5 [ 1 `' Y7 Q$ {) v' Yclass Gene:$ O- Q& O8 X) G3 I" Z3 r. t
'''3 t: t/ i: j/ `8 [8 l: S; V; ~0 O" l
This is a class to represent individual(Gene) in GA algorithom 3 D" j0 `9 z4 d9 P+ [8 ?. P each object of this class have two attribute: data, size 8 M& I, M+ d/ T5 p '''; q1 |7 C! D6 A- g& @+ j. Y1 p+ k
def __init__(self,**data): w+ Q0 p9 |6 w7 ?+ R. I, f' r a self.__dict__.update(data) 1 u9 U' I# o" k- Q% Y
self.size = len(data['data'])#length of gene& {$ _, G h. K7 E
6 Q: v- P, G' l, _3 E2 F 4 {: }7 @3 a5 T( r9 K& zclass GA:! H8 c+ ?" f- a* T$ y( e$ n
''' , |: D* z# T$ V7 t. r. ^9 p This is a class of GA algorithm. 2 T5 X8 d: d0 F% Z K
'''2 c4 `- Q0 V3 E: {; m+ F0 g9 U
def __init__(self,parameter):' }; k) @$ n6 f, `! B; Q8 Q; W
'''9 v, p |+ ?' e; k6 a! e! P G
Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value . + n& M# R5 r7 s& o The data structure of pop is composed of several individuals which has the form like that: 9 @/ A+ T( \$ a1 ?6 G) O- Y/ Q+ N+ ^, J9 R7 i
{'Gene':a object of class Gene, 'fitness': 1.02(for example)}- @8 v0 ~" k9 M7 h, F+ u; }* [# R
Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2] + \! b6 ?% J( j7 e- d% B ' G# d* Z6 B& n1 i( B( C! V ''' / ^6 z- g! L4 c0 }. R- F- L #parameter = [CXPB, MUTPB, NGEN, popsize, low, up]! ?5 i' P& a _+ c, }- j. O
self.parameter = parameter : S. c. x2 c% J8 r # g8 K& S- ~3 t2 W8 \# } low = self.parameter[4] " K9 B* `4 l, h0 [ up = self.parameter[5]. Q8 ~6 r& E, s
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self.bound = [] ( H" A) d5 Z- H/ @. q2 a* N% Q8 |, A self.bound.append(low) 8 q2 x- Z% u o& V: c% \& A self.bound.append(up)1 W1 v- _1 ~! ?& p4 g4 X9 ^" S
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pop = [] 2 c& C: W4 [8 o0 [+ s$ |8 Y' Q for i in range(self.parameter[3]): 2 H+ U, e6 ^4 Q7 W6 ~& E3 G! i9 u7 ^ geneinfo = []! q8 @6 @+ N' f1 V
for pos in range(len(low)): ! [7 X. z, B4 [4 R+ I+ }7 v; i; b geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation " n) D0 T3 O* y. {: J& Y q7 E/ X& M9 ?6 f M
fitness = evaluate(geneinfo)#evaluate each chromosome. r! z! C! y$ c3 W7 R6 p
pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness 6 @: [0 v7 g3 v% w6 q. W! z" w/ _6 {8 n! b: Y
self.pop = pop8 C5 g' b8 `, h' G* v0 z1 W6 X
self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population. S$ j; O" {! j2 W
# c. A3 W5 G/ S def selectBest(self, pop): ) Y+ U) p! J2 s$ X& c ''' - f: c1 F% s$ Q; k9 t select the best individual from pop & b" X$ g- A+ w0 h7 z& ] '''( d* P' F$ u0 x. c$ p: I( W
s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False) ) D- \: U. C6 t' r return s_inds[0]$ k5 {0 T( r; Q4 }4 j
5 n& @: M( s8 I$ e' z8 \ def selection(self, individuals, k): $ R8 j2 H9 t7 H ''' y! W& h4 h% a* J# u select two individuals from pop& X7 }* z% [% j$ Y' l2 A
''' 6 t" b g. G+ }! I s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness 2 K0 Q; g! Z, ?" D( a' X sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop" \* i7 p( Y2 I& J l
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chosen = [] 9 a; f2 Q V2 M# [+ \; | for i in xrange(k): 8 ^4 g) }: p1 f) U9 [7 p6 l u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits] , K( T4 L. u2 {+ b. \: T sum_ = 0 N; W1 A! a$ d for ind in s_inds: & g* }- C9 }+ C: m& q2 d8 B sum_ += 1/ind['fitness']#sum up the 1/fitness: `' A2 f; t! B" g
if sum_ > u:/ \* ?) q3 `3 V6 o" Z8 O' 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 , F7 w& S- r, r chosen.append(ind) 8 y0 f% e! E) E7 x5 Z break i( ~) p+ L) A) \1 c4 S# `: N
' Z+ M% G! o% z$ w: Z. [ return chosen # W7 \- z$ d+ s9 W9 z5 Z9 a , X, r, s" t, U" }2 Q# H7 Z 3 h. L+ F1 r+ m% _1 c% }+ z- [ def crossoperate(self, offspring): 9 |: D' Y; Z/ K1 k1 m0 o ''' 2 R8 E+ q1 N! f% [ cross operation% i7 y4 G) C' a' C
'''1 Y4 ~$ w) o9 z/ \- r6 m- r. i
dim = len(offspring[0]['Gene'].data) ; x' u1 f. y/ t8 w& a# c+ M: j- ^% ~4 d- |' x3 T* z
geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop ) T1 e9 q& A% i+ c" H, W; m geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop ) h2 @6 g- Q. E# h' @5 H$ u2 A& \. l# p E
pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, 3 }0 B5 V4 `& C; m
pos2 = random.randrange(1,dim) 8 t3 D+ S; h, p1 s9 e& J: R; n( F4 f4 X. \5 ^
newoff = Gene(data = [])#offspring produced by cross operation ( t: b/ b* `/ `% @, y temp = [] 7 b; o; r/ }; a for i in range(dim): 9 N6 m* V/ j: [ if (i >= min(pos1,pos2) and i <= max(pos1,pos2)):( ^! o( r2 S$ H
temp.append(geninfo2)0 f$ M: g2 Z) n a3 F! G1 l; g
#the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)]8 x6 [$ E v1 T) O
else: % W& q, J |4 g( T% N temp.append(geninfo1)7 v1 A( h/ |( b. `' [
#the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)] : }6 c2 v1 q, M2 g3 G newoff.data = temp ! T: B5 J4 \, P& z2 g# ~1 G5 k, I, ?$ l
return newoff$ ~) W. u( N+ P. u
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def mutation(self, crossoff, bound): / c$ b" V& S9 \) R. j" } '''6 m: R' Q! k0 y x
mutation operation$ t2 m, \5 A/ ^0 M
'''6 i c1 i+ z! h# ~' j# W
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dim = len(crossoff.data) $ }9 z3 M- {3 r4 {! X0 |# \. C6 H3 i8 S' o6 {
pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation. 9 C3 p. a+ J8 V. T: U/ o2 e 1 u4 ]0 C* u) c) Q crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos])% }( w- L7 t. S! l' v) |$ M
return crossoff3 X5 b' `' e7 w* B
8 U @( x* B9 }- C$ W def GA_main(self): / h: c; Y' O8 O9 v2 J- F4 [ ''' $ r# G8 {, m+ ?! b7 ~; ` main frame work of GA * S/ p) s' T: }% l2 f8 ?0 V ''' ( U" Q* W _ K3 T( I; t) U% ] # \9 _3 u9 T! Z, J5 ?, s* o" \$ N popsize = self.parameter[3]& A* i+ o+ f- O( h
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print("Start of evolution")$ w2 ^3 K- {2 Y4 ?' Z( ?4 O% E0 C
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# Begin the evolution$ T( F8 l8 I, x
for g in range(NGEN): + h( y0 F4 n5 r' N. y/ ^0 t5 q* C `! C& Z$ a4 s1 T
print("-- Generation %i --" % g) : I6 ^! d5 S% r# L
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#Apply selection based on their converted fitness + S$ {8 x/ u& ]# S- K selectpop = self.selection(self.pop, popsize) ( D" ]3 g7 n G- c 0 i. P9 |0 j5 z% ?% D nextoff = [] 2 W7 S! p# O+ T& h& s
while len(nextoff) != popsize: i' o5 {8 t- K3 h2 _
# Apply crossover and mutation on the offspring + |/ V; j1 K. s# z/ o& @8 f: Q. g% F ( V" ^3 l/ b& W( Z: K% p4 Q% y" M # Select two individuals" I2 ?! z0 \/ R5 M3 E; G9 S
offspring = [random.choice(selectpop) for i in xrange(2)] " U* O: f5 N9 V, t6 }" `7 K% i# ?1 P$ v
if random.random() < CXPB: # cross two individuals with probability CXPB6 l# d0 O S2 u$ T5 v* P
crossoff = self.crossoperate(offspring) 7 Y* T) N5 O- X) C, C Z( C fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals , K) B. |. K: A" p, f
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if random.random() < MUTPB: # mutate an individual with probability MUTPB 4 V( e2 g, ]& _. r4 S$ v. u muteoff = self.mutation(crossoff,self.bound) & r- e# T$ S* P) a& l8 Y/ p& Y fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals ( M6 p$ u0 [% Q nextoff.append({'Gene':muteoff,'fitness':fit_muteoff}) b1 ]8 r' z$ H& T0 q" Y% } 4 \4 X3 x2 u$ d' A# G6 o # The population is entirely replaced by the offspring6 i- n( |% R* N1 Z& O$ T
self.pop = nextoff & a0 Q5 o D1 S, m$ f: U- l; z. F/ k8 W
# Gather all the fitnesses in one list and print the stats: p4 x4 r( `. A O" w) M; P
fits = [ind['fitness'] for ind in self.pop]$ q& V" j5 G$ q! Q
$ `' M8 `# }; W9 A length = len(self.pop)) O) y8 ~+ |, ^' p$ }0 i; u
mean = sum(fits) / length H( r1 I5 f* {5 w
sum2 = sum(x*x for x in fits)) Z' M& c5 w1 O# q0 U7 U7 I
std = abs(sum2 / length - mean**2)**0.5$ Q( O; G9 @* G1 U
best_ind = self.selectBest(self.pop) 4 n0 E U# j) h% p; A. N # d9 q% L8 q; k. a3 U1 { if best_ind['fitness'] < self.bestindividual['fitness']: " V% G6 L5 S {/ [- u self.bestindividual = best_ind. T% W+ G0 k. v: p1 c; \* X
: n0 n) h! S6 L) u7 v print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness'])) % A& @2 d) k. j; p% `4 @5 G5 e print(" Min fitness of current pop: %s" % min(fits)) 9 }: ^! R- l; E/ i! @! G4 \ print(" Max fitness of current pop: %s" % max(fits))$ q$ ?' C. c9 f$ |
print(" Avg fitness of current pop: %s" % mean)! g9 e0 g5 o, g
print(" Std of currrent pop: %s" % std) ( p& J9 \1 j8 T * j0 e# d$ z' k# f$ ?$ m print("-- End of (successful) evolution --") ' [; m( b3 u; f& U8 J' s7 B" e$ Q. C7 t& e
if __name__ == "__main__": # _. A6 b9 Y) I1 Z) s+ N; U0 N9 w$ G2 w; f' M" c2 g
CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters - A; y# Y& C$ T3 B" _, |6 [' A, F' y( u
up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables7 j' Z0 }& F) N# E- h7 Z
low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables 2 F+ B8 D% ]% N7 W4 |# L parameter = [CXPB, MUTPB, NGEN, popsize, low, up], F' X; z4 q+ ]( _) Z4 ~, x
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run = GA(parameter) & R" \* ~+ E) N& I run.GA_main()% A' K, i2 E6 ]4 ~9 N2 y' X8 _! I
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