9 C$ L6 ^9 S( R8 ?9 [: s4 I4、Python代码) H1 s$ r, n$ U' n/ X( @
#-*- coding:utf-8 -*-: w# g+ N: G6 M! z7 }# q
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import random/ z+ P, T! a4 n4 a
import math6 l$ _& H' u; N6 V7 Y) h
from operator import itemgetter & X9 d8 `+ h/ t) X( Q* \# v 8 x/ u9 Z& Q9 e" W8 l; ~" _class Gene:; w0 f* r$ Z/ b+ l3 l+ }( h
''' 3 T9 A" y* n% L! D, [3 E This is a class to represent individual(Gene) in GA algorithom . ]- S; |) }0 [ each object of this class have two attribute: data, size: c4 h+ ~( T0 I( l, Q4 z
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def __init__(self,**data):$ K) k7 O0 X- O. w7 R5 o" ?3 ^, J: y
self.__dict__.update(data) , u! I: P* K* Y# g
self.size = len(data['data'])#length of gene ' w, l5 B2 p. n `1 x" \+ j2 P }) R$ [
+ j) ] S) v0 Eclass GA: 5 W4 q) `1 A; u, G7 Z& m& B ''' + y% b7 Q/ h( d) R This is a class of GA algorithm. 5 F8 U* M" c* f: F+ I
''' ( ]. S1 L0 d. @( }7 o7 w v6 G def __init__(self,parameter): & A2 u2 i/ o, l) q. | '''. J. x4 @( ?( `8 h H6 A, a
Initialize the pop of GA algorithom and evaluate the pop by computing its' fitness value .7 h9 b+ F; ~6 w2 \2 V0 \5 ~6 n
The data structure of pop is composed of several individuals which has the form like that:* X: G0 Y, [8 Y4 q; V0 r
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{'Gene':a object of class Gene, 'fitness': 1.02(for example)} " x- V9 V! s7 M0 G$ } Representation of Gene is a list: [b s0 u0 sita0 s1 u1 sita1 s2 u2 sita2]* W' V# F# N$ T- R) x
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''' " Z) K; |% Q( T; {" ^9 z; n! ] #parameter = [CXPB, MUTPB, NGEN, popsize, low, up]& |* N! a) }7 M0 B Q X" \6 K
self.parameter = parameter3 Y% g: r- r& }/ a# v, R" |
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low = self.parameter[4]! D* I: ]& K& G7 s3 z
up = self.parameter[5] 5 v8 n- m! B# c # Y$ r1 g- \, t self.bound = [] _5 ?, M3 `, }, z) }5 H" }+ v. k0 F
self.bound.append(low) ) b: V# t# Z5 ~5 u self.bound.append(up)6 z1 J6 V: l7 ]6 X9 |5 e
, ?3 d% N) H$ k( } pop = []. Q# [: p& l7 \" ^1 _" ]
for i in range(self.parameter[3]):9 g8 x0 O3 @4 q7 s% y1 A
geneinfo = [] $ S/ M& R# W( j2 w' e) r+ F for pos in range(len(low)): ; n* X* a8 a. B( B& J' g geneinfo.append(random.uniform(self.bound[0][pos], self.bound[1][pos]))#initialise popluation ) w2 g7 {: J% \' x6 ?! u ! K) }% |6 y! ` v% B+ B+ O fitness = evaluate(geneinfo)#evaluate each chromosome$ M0 z2 |" i' P* `# t8 X
pop.append({'Gene':Gene(data = geneinfo), 'fitness':fitness})#store the chromosome and its fitness 6 D% Y1 ~+ l& j g8 Y$ v1 u6 M8 P: a1 B1 z d4 J
self.pop = pop - g) B* y$ t1 T2 ]/ c$ C1 v2 K self.bestindividual = self.selectBest(self.pop)#store the best chromosome in the population $ |) b) U. q v9 L4 E3 ]# C/ W, I" i. a7 u: P5 E C* {
def selectBest(self, pop):2 X. d, { m# ` D$ H
''' # N R+ n8 M+ U4 C select the best individual from pop 4 U& o) M' S" m6 U" R9 Z '''9 Q5 A# ]# H# N* M, R
s_inds = sorted(pop, key = itemgetter("fitness"), reverse = False) # I/ P5 q: p; O' z return s_inds[0] - ~, E, y" J5 ^9 k$ S" v5 ^, m5 v. Z' ]0 v# s+ v3 Q
def selection(self, individuals, k):% X# I8 |3 r( H' B
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select two individuals from pop 8 ~8 }2 ~% Y' T: V" c4 A '''9 [( F) j. P$ T' @( Z
s_inds = sorted(individuals, key = itemgetter("fitness"), reverse=True)#sort the pop by the reference of 1/fitness * N; U0 t3 V# G: K, T sum_fits = sum(1/ind['fitness'] for ind in individuals) #sum up the 1/fitness of the whole pop ! G! A1 \* N. Y# d + V9 n# d" p8 H0 ?9 N chosen = []! U' P; d0 s# ~# J; \
for i in xrange(k): ! F" R/ z- `) t$ Y% `* Q u = random.random() * sum_fits#randomly produce a num in the range of [0, sum_fits]5 \% J# l; A( b" T! ]9 M8 P
sum_ = 0 ! I2 |# E. S0 q9 ~6 G for ind in s_inds: & ?- G3 J. v/ U' ~$ ]/ c% ^ sum_ += 1/ind['fitness']#sum up the 1/fitness 1 ^8 _/ b0 @" T$ G: [; S if sum_ > u: $ D5 W& |. l$ }1 } #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( b8 j/ j& c5 b. L% \$ q; W# D0 }$ H
chosen.append(ind) - ?1 i. w, D/ x4 j6 P1 o break ; n8 B/ M2 c( ] 5 `: |6 o! H( J. r( S return chosen & O7 _4 e) y7 T; N2 J. `, T
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' {$ I G. ?* i; _ def crossoperate(self, offspring): 4 L7 X E; k4 c '''2 \1 g$ o0 |% i/ X7 x% O
cross operation ( L0 |, }/ O% i. }6 d '''# C Y$ [3 [- k* l/ Q D8 i7 M
dim = len(offspring[0]['Gene'].data) & `9 V& a. y- y1 X' `! G7 N/ E% [
geninfo1 = offspring[0]['Gene'].data#Gene's data of first offspring chosen from the selected pop % G: \6 M' y+ ?% q# r geninfo2 = offspring[1]['Gene'].data#Gene's data of second offspring chosen from the selected pop: e' B! H6 J9 U! T% T; e, C2 e( t& p
9 Y2 I% T8 q9 {0 @9 q/ U) ^9 b" m pos1 = random.randrange(1,dim)#select a position in the range from 0 to dim-1, 3 }4 q, V" ]* _1 S" Y# b8 T; g
pos2 = random.randrange(1,dim) ! [7 O. y k' m$ T9 H* T6 Z7 O : g1 f$ U) R0 I' m z- O$ b% X newoff = Gene(data = [])#offspring produced by cross operation5 g3 S7 P) Z* J
temp = [] : G- F9 W% q: m3 g# g2 ]- J x; ?6 b for i in range(dim):0 X9 h4 z3 Z# \9 ~9 w
if (i >= min(pos1,pos2) and i <= max(pos1,pos2)): ) Z6 Q$ n( h) A3 O6 o4 `! p1 y temp.append(geninfo2) 3 A% c. h) R7 k3 F4 [8 x' J. t! B #the gene data of offspring produced by cross operation is from the second offspring in the range [min(pos1,pos2),max(pos1,pos2)], n/ i a' x E
else: / a0 C1 R% C) S temp.append(geninfo1)" Z7 j$ [- {, w p) S. f( y. L) G% t
#the gene data of offspring produced by cross operation is from the frist offspring in the range [min(pos1,pos2),max(pos1,pos2)] 3 ^. G1 O, z. z* E3 B0 C/ M8 ]* A4 z newoff.data = temp ! s% d( v$ ?+ e) K7 b& e2 s) j ' R: P6 Y( G, p return newoff. j- [" o& D. b! a Y4 U( r9 k
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def mutation(self, crossoff, bound):1 Z+ w6 ?, z5 M0 j6 H3 S: F
''' 4 T' ?, |: b/ p( J& F7 K+ o8 Z mutation operation: l+ @! n1 {; C) k/ C. k
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dim = len(crossoff.data)$ Z E E7 g) U+ D( g
) z6 L$ j. \5 p* _9 M- o0 { pos = random.randrange(1,dim)#chose a position in crossoff to perform mutation.* x, Q, \1 q6 l, o6 n& K/ X9 q
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crossoff.data[pos] = random.uniform(bound[0][pos],bound[1][pos]) - e8 J5 L6 O4 c1 r; o return crossoff* V9 B* r$ J2 ?
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def GA_main(self): : ^' U3 `# I# W. n; V ''' ( X% R+ q- O# B% a; W main frame work of GA6 L$ j' ~$ @! _) l5 @
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& g- x: X# {2 o& v7 K popsize = self.parameter[3]4 d& w' f" w% C6 H* x/ [- Q
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print("Start of evolution"): R! S( F2 E5 _8 ^! H- J. R
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# Begin the evolution " v" q# |0 s1 s$ W/ h- Q+ u for g in range(NGEN): / T5 A' o/ v" P 0 E' H+ h. T2 v) q2 n print("-- Generation %i --" % g) . W' V, ]: b4 E* E2 @4 Z7 ^
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#Apply selection based on their converted fitness 5 \/ M! g& @1 O8 U- a( @ selectpop = self.selection(self.pop, popsize) 5 G+ ]8 W0 {2 C: T/ r! p ( n# m! p0 n/ M/ I N5 p! f3 j nextoff = [] * [: k6 t9 o2 ^4 e$ a2 R
while len(nextoff) != popsize: - v2 e3 `/ d+ D0 e4 Z; I
# Apply crossover and mutation on the offspring ! B8 n* P; Q: @) v# G; p# I! u' X% T; }8 H4 V& s
# Select two individuals1 V6 q3 B% I& h& V9 F
offspring = [random.choice(selectpop) for i in xrange(2)]3 ^% U- G- m; H. c" |2 F8 Z
" g: q+ \. X' f* M; Q j if random.random() < CXPB: # cross two individuals with probability CXPB 7 ?$ G$ C+ s8 k; ~ crossoff = self.crossoperate(offspring) 4 z. a/ K$ E* p h/ g) | fit_crossoff = evaluate(self.xydata, crossoff.data)# Evaluate the individuals " p' ~: `) a/ q3 S4 ?1 |" H
% L5 }7 U& s/ q8 P5 J if random.random() < MUTPB: # mutate an individual with probability MUTPB * s9 y7 z+ M' X7 \ h* O muteoff = self.mutation(crossoff,self.bound). z% z5 R3 Q5 T# R8 t
fit_muteoff = evaluate(self.xydata, muteoff.data)# Evaluate the individuals 6 O% q1 b$ {! i: S) i+ r1 c. C- C nextoff.append({'Gene':muteoff,'fitness':fit_muteoff}) 1 N. k4 @: j" x3 p' T 7 O2 g* s2 C+ N) X; A5 i) w* @- U # The population is entirely replaced by the offspring 7 n, F$ C9 |% q2 P( x2 s self.pop = nextoff4 Q% X* i2 [9 K; z
9 C! h, s- ^# t# j, P # Gather all the fitnesses in one list and print the stats ; e7 z& d, c/ ~; |) u fits = [ind['fitness'] for ind in self.pop]9 z# d3 d& e" l9 `# v
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length = len(self.pop)7 E" P" K! O: d! K+ z* ?2 p4 g- s
mean = sum(fits) / length6 o! k" I# g5 \( a
sum2 = sum(x*x for x in fits) , O' s/ k0 W9 `& w: r std = abs(sum2 / length - mean**2)**0.5' v' x( m: @( ^# c [ t6 n" p& `) e" B8 X
best_ind = self.selectBest(self.pop)$ W! s. i0 U$ o9 k
0 Q9 V/ a9 I% e% S if best_ind['fitness'] < self.bestindividual['fitness']: . b# u! M0 |$ s# T! P& t self.bestindividual = best_ind 8 r$ x5 s! S/ F8 \# M. L- E 0 y' O" R" s$ l" y print("Best individual found is %s, %s" % (self.bestindividual['Gene'].data,self.bestindividual['fitness'])) 8 d3 y3 y9 v2 T! ` print(" Min fitness of current pop: %s" % min(fits))- M# y) b0 e i7 X
print(" Max fitness of current pop: %s" % max(fits)) 6 J; K# j7 y! X( b print(" Avg fitness of current pop: %s" % mean) ) [* M; T6 |3 t print(" Std of currrent pop: %s" % std) 5 ] V8 z6 V7 p" R% _! z/ j( X2 `, }, F/ F$ f- l
print("-- End of (successful) evolution --") : Y' d- [# P1 ]2 }, {( k ) a: V) m3 ?1 ?* t1 U' S, j: ]if __name__ == "__main__": ' E) ]2 h& ?- v H7 F; ~6 I- B% Z' o! s$ q' k, n- {$ w, Y
CXPB, MUTPB, NGEN, popsize = 0.8, 0.3, 50, 100#control parameters - K. W6 h8 Z8 O9 R4 |: n. X$ L
up = [64, 64, 64, 64, 64, 64, 64, 64, 64, 64]#upper range for variables / N$ g' X' \* Y. ^) _% k) ] low = [-64, -64, -64, -64, -64, -64, -64, -64, -64, -64]#lower range for variables % a4 F- [2 }/ R/ | parameter = [CXPB, MUTPB, NGEN, popsize, low, up] 0 p8 Q- i J( m / g. ~7 a) r; c4 ?% J* \# j run = GA(parameter)' r- |# m" ~6 A( J( ~, `
run.GA_main() 7 T, I, O- I/ g7 t———————————————— ) d! ~4 L9 a( s5 a7 ?: v4 j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。; ? f4 {0 j5 U3 x& d3 O7 _$ @
原文链接：https://blog.csdn.net/bible_reader/article/details/72782675 4 A" Q& L! b, F# ]1 I, }1 d% \* T w1 n4 Z; O; e! V( i4 d2 J4 x
