# 1.- Create random population 100 chromosomes with 20 genes each
population = helper.createPopulation(list(range(1, 21)),
travelParms.population)
distances = helper.calculateDistancesInPopulation(population)
#print("-------INITIAL STATE------")
#helper.printPopulation(population, distances)
# 1.a- Call graphs and graph the best
# First generation
helper.createGraphs(population, distances)
for i in range(travelParms.iterations):
# 2.- Compete, get chosen parents
chosenParents = helper.getChosenParents(population, distances)
# 3.- Reproduction, replace population with children
population = helper.getNewPopulation(population, chosenParents)
distances = helper.calculateDistancesInPopulation(population)
# 3.a- Get the best and graph
helper.updateGraphs(i + 1, population, distances)
# 4.- Print and graph the best of all times
helper.printBest()
plt.show()
#print("-------FINAL STATE------")
#helper.printPopulation(population, distances)
ecuaParms.maxR, ecuaParms.chromLength, ecuaParms.popuLength)
matrix = helper.calculateMatrixResults(ecuaParms.result)
results = helper.calculateResults(population, matrix,
ecuaParms.divisor)
# 2.- Create graph
helper.createGraphs(population, results, matrix, ecuaParms)
minError = min(results)
mutationP = ecuaParms.mutationPercent
# 3.- Iterate
for i in range(ecuaParms.iterations):
chosenParents = helper.getChosenParents(population, results,
ecuaParms.percent, ecuaParms.parentNum)
population = helper.bitExchangeReproduction(population,
chosenParents, ecuaParms.parentNum, ecuaParms.bits)
helper.mutation(population, mutationP, ecuaParms.bits)
results = helper.calculateResults(population, matrix,
ecuaParms.divisor)
newMinError = min(results)
if(minError == newMinError):
#mutationP = 20
helper.addRandomnes_1(population, ecuaParms)
#results = helper.calculateResults(population, matrix, ecuaParms.divisor)
results = helper.calculateResults(x, y, population, zResult, 51)
minInd = results.index(min(results))
# Get best matrix to graph
bestChrom = helper.divideByFactor(population[minInd], 51)
zBestChrom = helper.aptitudFunction(bestChrom, x, y)
# 3D printing
#print(zBestChrom)
fig = plt.figure(figsize=plt.figaspect(0.5))
ax = helper.graph3D(fig, x, y, zResult, zBestChrom, results[minInd])
acumError = results[minInd]
# 3.- Iterate
for i in range(iterations):
chosenParents = helper.getChosenParents(population, results,
tournamentPercent, 2)
population = helper.bitExchangeReproduction(population, chosenParents, 2,
8)
helper.mutation(population, mutationPercent, 8)
results = helper.calculateResults(x, y, population, zResult, 51)
minInd = results.index(min(results))
# Get best matrix to graph
bestChrom = helper.divideByFactor(population[minInd], 51)
zBestChrom = helper.aptitudFunction(bestChrom, x, y)
helper.updateGraph3D(ax, x, y, zBestChrom, results[minInd])
print("Iteration:", i, "Error:", results[minInd])
#print(bestChrom)
from helper import helper
from constants import ecuaParms
# Test 'createPopulation'
print("Testing createPopulation")
population = helper.createPopulation(0, 255, 3, 10)
results = helper.calculateResults(population, 10, 2, 13)
print(len(population), len(results))
helper.printPopulation(population, results)
# Test 'getChosenParents'
print("Testing getChosenParents")
chosenParents = helper.getChosenParents(population, results, 5, 2)
for i in range(len(chosenParents)):
print(i, chosenParents[i], results[chosenParents[i]])
# Test 'intToBitArray'
print("Testing intToBitArray")
bitArray_1 = helper.intToBitArray(3, 8)
bitArray_2 = helper.intToBitArray(15, 8)
bitArray_3 = helper.intToBitArray(8, 8)
bitArray_4 = helper.intToBitArray(255, 8)
bitArray_5 = helper.intToBitArray(0, 8)
print("3", bitArray_1)
print("15", bitArray_2)
print("8", bitArray_3)
print("255", bitArray_4)
print("0", bitArray_5)
# Test 'bitArrayToInt'
print("Testing bitArrayToInt")