* f: A# x+ N! s+ Q+ \2 |$ Q ' \5 F1 `2 ?- u& d4、Python代码 / Y! [3 m; J4 Z7 [#-*- coding:utf-8 -*- 0 j; M6 ]$ } I ^. ]1 n4 n% B % }3 f! u7 L" g8 _9 X: dimport random' t- I6 s( ?% L8 m' M$ m! R! V
import math8 [4 f2 `/ [* s
from operator import itemgetter3 r5 y, o! k# x. c8 b
; ~( u+ j, Q5 vclass Gene: 8 n4 v9 p$ ?/ g3 t: q$ q '''* o; T5 J1 Q" B( f- [ a
This is a class to represent individual(Gene) in GA algorithom ! x0 q! Q y/ I+ q- B5 s each object of this class have two attribute: data, size- s3 l: X" v- o$ d N
''' " v- Q9 w) w/ m# t, k7 [5 { def __init__(self,**data): 8 f5 m+ f5 G2 v) Y+ R3 R; G self.__dict__.update(data) & ~3 O) |9 `2 {4 B! K
self.size = len(data['data'])#length of gene3 x8 c+ z# a, [' A
5 I/ G3 Q% u, ]. A& `# h
. ]' m4 h* Z# b" w" N& F: O
class GA: ' [2 T2 e& }8 v) C( ?4 x '''9 s( O2 H3 h) ] @) Q/ }3 Q
This is a class of GA algorithm. * A6 [4 e! S" G* G* ^) R; U7 X
''' f( ~; }' b4 @6 v
def __init__(self,parameter): 6 T/ \6 t' |5 L6 s$ G2 b8 d '''3 y/ E8 ^, N8 k8 j& | Y+ ~
Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value .7 l! V* U$ r( W7 x
The data structure of pop is composed of several individuals which has the form like that:- U( O5 p% Y; g9 f3 k5 X; }2 A
% t6 S; I J. \% `$ W8 d4 p {'Gene':a object of class Gene, 'fitness': 1.02(for example)} ( V1 ~. V- B' b+ G4 _2 K Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2] 7 b( M o) Q2 m1 v' w( t+ _ 3 S$ T3 `; c' X' I '''1 v% U9 R7 }& A% y0 `! ^7 f
#parameter = [CXPB, MUTPB, NGEN, popsize, low, up] 5 k4 n$ e+ h1 l self.parameter = parameter 6 y' g5 v; M* B& o& }4 E 4 g+ m5 o& | {, Y# N low = self.parameter[4]- b1 I4 T& k7 Z- A# g* b
up = self.parameter[5]' @4 k: U3 h7 {1 J+ X& ]
) W1 R' K$ q& i
self.bound = []1 H4 V" ]0 X! ` @- O# f3 @/ s
self.bound.append(low) - L; o9 \* W y/ ] self.bound.append(up)* e7 q$ L( t9 X# V3 y2 t! f5 }
: x$ f: s, R; ~ pop = [] 4 s: w3 E3 J K; U1 G7 o for i in range(self.parameter[3]):. L4 k, z( r9 M' ~8 `- z$ ~( D3 P$ I
geneinfo = []5 N2 n: z) T2 O- t5 E- y* ?7 c
for pos in range(len(low)): : f# Y# b& m. a" C geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation 4 y0 U0 l+ z5 K6 S7 e; w+ A ( e( A; x3 |* Q& }. d fitness = evaluate(geneinfo)#evaluate each chromosome* V- s! ~' h, d/ y7 _/ y+ R
pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness % n% }. C8 ~2 h " W% l$ ?4 b. {& w& D# S self.pop = pop 0 F+ B, l& u8 I( D8 Y: [; ]! Y* ? self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population% q$ r0 q+ O! H' n7 l# n4 s3 n5 w$ D5 L4 g
8 ?3 s$ T- u- h1 Y0 S- A/ T
def selectBest(self, pop): 7 h0 O9 _! I6 a' Q" G ''' ( ?: [4 n- s% A select the best individual from pop' v& T/ |0 L# F( d# O0 A) @+ a8 l
'''3 k6 |2 o$ Z d) w' H- r2 C
s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False)8 c+ [$ V8 g: E* v
return s_inds[0]0 [: X+ k: [2 G
6 ]5 C: t: d% @6 f# ^
def selection(self, individuals, k): . |( E9 j" _' y1 V ''' ) |, |2 b9 M/ @8 H- L' L" ~" ?5 T select two individuals from pop : J& r( {# }% F$ J2 i7 V '''# `0 D1 o3 U# o" l" M' q
s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness ; j$ A$ a% f2 |9 x
sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop " O# Y! Q0 r! ^* p" y/ v8 @1 D7 _, f' G D
chosen = []+ q+ n8 g9 ]# _3 t. _; M
for i in xrange(k): ( k9 A3 P2 N! g+ g" ` u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits] 6 d2 }; t: W8 v/ f; Q6 T! Z7 c! K sum_ = 0 2 V' P; I7 f' h for ind in s_inds:( B1 K3 c$ d& C2 c
sum_ += 1/ind['fitness']#sum up the 1/fitness# R. n9 U8 s5 P$ M0 z' m; s$ ]
if sum_ > u:' S4 s5 y7 y6 ]$ u0 M/ [
#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. Z# |" N4 X4 e8 k/ t* H
chosen.append(ind) ) c& s8 ]) J q# F8 v1 W break 3 E4 I3 O( C+ A- q * [# |4 t$ z/ r* t return chosen 3 M! M+ |* J; j' X/ d: o
) _( P& h: k1 |; @$ z. A 0 ?) a' n9 r1 ?. b def crossoperate(self, offspring): - _+ n5 P& q" N5 T6 Y3 J ''' # e! A6 A6 k! W0 W$ B. L cross operation 7 L! s( u8 o8 P '''7 \3 N) M# p9 l" {% |
dim = len(offspring[0]['Gene'].data)9 }& ]9 H8 U' ]) S( k
. R1 t& F& z; q. ?3 K
geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop: _9 F/ |. I( P( w( r P5 C0 k
geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop6 p8 r `0 _& r. y3 Z
- M9 _- }4 R: g" q# l5 W7 n M
pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, : _, Z( L0 C" M9 P3 W) g, w6 \0 | pos2 = random.randrange(1,dim) U9 s4 k+ _; `' j- j
8 g: X. x; b+ I4 y# J s: Z' A/ @
newoff = Gene(data = [])#offspring produced by cross operation, Y" u% ~0 ?9 u& ~
temp = [] * V$ G4 d! `. i) Y3 b2 V# G for i in range(dim): - [3 @2 @8 I. |1 `: | if (i >= min(pos1,pos2) and i <= max(pos1,pos2)): 6 J9 q( y( {, U& s temp.append(geninfo2) 7 Q$ |( k- ~8 G3 Y/ m! m7 j$ c# q' L #the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)] ; _. l* H7 x& d1 a# s" D else:3 ~, G7 w0 }# o! o7 ~6 p& Q
temp.append(geninfo1) 5 d1 \5 e7 D& X5 \* A #the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)]5 O, E# L7 g2 b1 \1 P
newoff.data = temp# s! l" E. x" d! r5 S, d
0 C9 e0 ]7 J0 A0 S6 x/ S& T$ Z return newoff- i4 A' P l, ^
" [2 _# Q* o F$ W+ W; c5 H- o) Y8 J. i9 a# y4 G! _
def mutation(self, crossoff, bound): ~+ l0 p5 h+ f2 b8 Z& l
'''( C& K) o0 E9 u0 j! m8 m6 S" L
mutation operation, X& X K4 J; O: _' q
'''5 H! { g2 o+ d: Y- ]2 S
, j; B4 l, f+ ^) b1 R/ l" u' q, i dim = len(crossoff.data), {# k! D# }( d# z ^; l' y- @
8 @) b; P& s3 ?5 B5 \ m9 p2 U2 @" |+ ^ pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation. 5 x% y# W4 x" h4 }- T. t# U/ T) M8 T, o; ?" v
crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos]) . [& j6 c2 v5 U# Y% A return crossoff. r1 F) D9 K6 m: e, \
- a2 j! I) X2 G2 y def GA_main(self):1 R, I: w1 s: q p+ f: a7 U
'''; k2 z5 C v- s. y& m/ g* A
main frame work of GA 8 H4 C& O+ r5 j8 k ''' B( W d" `' W9 c$ @) n$ \' o2 ?) ~ ! E1 {& o) O( G1 ~6 L3 o popsize = self.parameter[3] : Z6 w! |( U: \ ?8 q# ?. r0 a3 }$ [
print("Start of evolution") - I% j" Q8 f9 ~0 E- U* ?( f' B* v$ }$ g1 k
# Begin the evolution 6 Q$ Z% C, o6 |' g2 i9 u1 T for g in range(NGEN): , x* T+ a; u, G/ `& f, N9 |5 k6 C! |. d( E
print("-- Generation %i --" % g) 6 P k- u7 B- ?/ S+ M6 k0 J
1 U: G9 n3 J" h+ e# q #Apply selection based on their converted fitness. i- P" g3 p+ U& P) D8 A
selectpop = self.selection(self.pop, popsize) ) ~- j( ^. h: c! I5 J! r9 }
* x0 p/ h0 ~; p, ~4 J
nextoff = [] 6 r9 U! `0 ^' h/ T1 z while len(nextoff) != popsize: ( K' p; _" [7 E # Apply crossover and mutation on the offspring . l9 }% o( _+ Y, Z3 L$ ~2 f. l6 }: O" q Y$ U
# Select two individuals # k6 t) W, A7 Y2 ^; Z5 O- | offspring = [random.choice(selectpop) for i in xrange(2)]& k4 T' y2 s* T$ t4 g; z! G5 `
0 ~$ @$ K4 P7 R- u" j: ` if random.random() < CXPB: # cross two individuals with probability CXPB . e+ g& B, b; R- Q0 u+ i crossoff = self.crossoperate(offspring)& b H1 `, @ P/ u& g
fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals ! X7 @: S/ e7 U$ P# j
: {8 a6 K% ^: j; U' N2 f if random.random() < MUTPB: # mutate an individual with probability MUTPB 5 t) M5 Q! G% z0 ]: M, C muteoff = self.mutation(crossoff,self.bound)1 ^( |* o! s, Q& C0 f' z
fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals $ b2 O5 K% w2 N' n/ B nextoff.append({'Gene':muteoff,'fitness':fit_muteoff})6 L. U* F2 Z- O6 X& g
- W, d8 ], c( W, |1 ~
# The population is entirely replaced by the offspring8 V1 t* d1 }% H, F3 U
self.pop = nextoff" O. d9 d; M" v) R
' K& ~; D! o0 w
# Gather all the fitnesses in one list and print the stats9 A7 K8 _/ l( g# l) l) D
fits = [ind['fitness'] for ind in self.pop] ! ]5 m/ Q+ O% c+ h* [, u( c/ p& D2 ]2 i+ _/ J$ I
length = len(self.pop)% J4 D5 O. ~8 {, E: @
mean = sum(fits) / length( }" A |) I `# ]) [
sum2 = sum(x*x for x in fits) 9 L$ T7 v# s/ \ std = abs(sum2 / length - mean**2)**0.5: p# I o% `; ^% x0 E. u
best_ind = self.selectBest(self.pop) 3 Q$ j7 O7 c- v4 @; F2 y2 p- V& X8 h& o& T; N d/ p" z3 y
if best_ind['fitness'] < self.bestindividual['fitness']: 0 f: P+ g8 A$ r self.bestindividual = best_ind 1 d1 k( O2 ~/ T& e! q3 x+ U: T9 Q& \' Y9 G4 W
print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness'])) - s+ L; {" V( _4 a print(" Min fitness of current pop: %s" % min(fits))+ A& c1 x# x' v/ s4 I, ]
print(" Max fitness of current pop: %s" % max(fits)) $ ~6 z3 m% I- u+ n% U print(" Avg fitness of current pop: %s" % mean)2 K1 d+ Y; y, w0 b- r6 F3 i: g
print(" Std of currrent pop: %s" % std)0 y0 S0 n( W4 w5 k$ V4 F
) u) n4 \( f* r5 P; c( I: q3 \- {
print("-- End of (successful) evolution --") 7 S; R# R$ v" M+ g- Q6 v; O
- C O6 A1 D( N# qif __name__ == "__main__": * K9 [+ z' A+ u f 2 ^( |5 v' l. j: }; J CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters 4 `* w* ]5 P- r$ ^5 c, o; s$ u r; I: p7 z1 V1 ~1 a6 \0 ]" z6 A
up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables1 k. N1 `' @0 B8 m. P5 i7 \
low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables5 |. S$ W& g' B
parameter = [CXPB, MUTPB, NGEN, popsize, low, up] " D3 c: D& r- t' L! D # ?# k/ k D' l: U+ T/ y9 M. I# Q run = GA(parameter)# U' g- j9 v9 c5 `% ?: u9 @, n3 B
run.GA_main() : y2 v: w. w! D; m1 A& @————————————————) Z1 H. R' @, p' X7 p2 s& l
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。 " n- b6 q$ y$ B) p) p" i8 b原文链接:https://blog.csdn.net/bible_reader/article/details/72782675 & E4 z1 A4 d" G- Z' F% l; |# H- v& o) w2 x* t$ }& R7 q9 |$ U
; V# U' K- L0 |, K