as of now it has found an optimal tour through London, Amsterdam, Hamburg, Copenhagen, Prague, Frankfurt, Brussels, and Paris
#!/usr/bin/python
import random
from pyevolve import G1DList
from pyevolve import GSimpleGA
from pyevolve import GAllele
from pyevolve import Mutators
from pyevolve import Crossovers
from pyevolve import Consts
#ideal tour: London, Amsterdam, Hamburg, Copenhagen, Prague (Praha?), Frankfurt,
#Brussels, Paris, London, length 2,980 km
# Amsterdam - 0
# Brussels - 1
# Copenhagen - 2
# Frankfurt - 3
# Hamburg - 4
# London - 5
# Paris - 6
# Prague - 7
# what is homogeneous?? resolved
# use special initializer
# OX crossover understood
distances = {}
# Amsterdam
distances[0,1] = 176
distances[0,2] = 623
distances[0,3] = 365
distances[0,4] = 366
distances[0,5] = 347
distances[0,6] = 431
distances[0,7] = 710
# Brussels
distances[1,0] = 176
distances[1,2] = 769
distances[1,3] = 319
distances[1,4] = 490
distances[1,5] = 308
distances[1,6] = 263
distances[1,7] = 716
# Copenhagen
distances[2,0] = 623
distances[2,1] = 769
distances[2,3] = 671
distances[2,4] = 289
distances[2,5] = 948
distances[2,6] = 1031
distances[2,7] = 642
# Frankfurt
distances[3,0] = 365
distances[3,1] = 319
distances[3,2] = 671
distances[3,4] = 394
distances[3,5] = 627
distances[3,6] = 481
distances[3,7] = 405
# Hamburg
distances[4,0] = 366
distances[4,1] = 490
distances[4,2] = 289
distances[4,3] = 394
distances[4,5] = 711
distances[4,6] = 747
distances[4,7] = 497
# London
distances[5,0] = 347
distances[5,1] = 308
distances[5,2] = 948
distances[5,3] = 627
distances[5,4] = 711
distances[5,6] = 337
distances[5,7] = 1019
# Paris
distances[6,0] = 431
distances[6,1] = 263
distances[6,2] = 1031
distances[6,3] = 481
distances[6,4] = 747
distances[6,5] = 337
distances[6,7] = 879
# Prague
distances[7,0] = 710
distances[7,1] = 716
distances[7,2] = 642
distances[7,3] = 405
distances[7,4] = 497
distances[7,5] = 1019
distances[7,6] = 879
# Number of cities
NUM_OF_CITIES = 8
def eval_func(chromosome):
total = 0
for i in xrange(NUM_OF_CITIES):
j = (i + 1) % NUM_OF_CITIES
total += distances[chromosome[i],chromosome[j]]
return total
def G1DListTSPInitializator(chromosome, **args):
chromosome.clearList()
lst = [i for i in xrange(chromosome.listSize)]
for i in xrange(chromosome.listSize):
choice = random.choice(lst)
lst.remove(choice)
chromosome.append(choice)
setOfAlleles = GAllele.GAlleles(homogeneous=True)
lst = [ i for i in xrange(NUM_OF_CITIES) ]
a = GAllele.GAlleleList(lst)
setOfAlleles.add(a)
genome = G1DList.G1DList(NUM_OF_CITIES)
genome.setParams(allele=setOfAlleles)
genome.evaluator.set(eval_func)
genome.mutator.set(Mutators.G1DListMutatorSwap)
genome.crossover.set(Crossovers.G1DListCrossoverOX)
genome.initializator.set(G1DListTSPInitializator)
ga = GSimpleGA.GSimpleGA(genome)
ga.setGenerations(1000)
ga.setMinimax(Consts.minimaxType["minimize"])
ga.setCrossoverRate(1.0)
ga.setMutationRate(0.03)
ga.setPopulationSize(80)
ga.evolve(freq_stats=100)
best = ga.bestIndividual()
print best
EVOLUTION IS A SATANIC LIE RIGHTnah just kidding I wouldn't say something dumb like that and mean it
that's only eight cities though
give me like twenty cities
I will find a nearly optimal tour for them
out of 2.5 quintillion possible tours on my craputer I will find a nearly optimal tour
really go ahead
