def steepAscent(length, iterations, fitFunction):
'''Finds optimal solution by steepest ascent method. Uses only one candidate, mutates it every iteration. If mutated
version is better, make that your new solution'''
x= generateBinrep(length)
optimal= candidate(x)
for i in range(iterations):
candCopy= candidate(optimal.bin_rep)
candCopy.mutate_switch()
if fitFunction(candCopy)>fitFunction(optimal):
optimal= candCopy
if (i+1)%100==0:
print 'In iteration ', i, ' max fitness= ', fitFunction(optimal)
print 'Fittest candidate is: ', optimal
def generatePop(popSize, bitlength):
population=[]
for i in range(popSize):
binrep= generateCandidate(bitlength)
population.append(candidate(binrep))
return population
def generatePop(popSize, bitlength):
'''Create a population of candidates of the given bitlength. Returns a list of candidates'''
population=[]
for i in range(popSize):
binrep= generateBinrep(bitlength)
population.append(candidate(binrep))
return population
def geneticAlgorithm(popSize, bitlength, crossProb, mutateProb, fitFunction, generations):
'''Executes a GA. First it creates a random population of size popSize, where each candidate's binrep is
specified by bitlength. Then goes through number of generations, with given evolutionary parameters'''
population= generatePop(popSize, bitlength) #Generate initial population
for genNum in range(generations): #each loop is one generation
maxFitness= 0
maxIndex= 0
totalFitness= 0
for i in range(popSize): #for every candidate evaluate fitness, keep track of fittest
f= fitFunction(population[i])
totalFitness+=f
if f>maxFitness:
maxFitness=f
maxIndex=i #note where the maxFitness was found
population[i].set_fitness(f)
averageFit= totalFitness/float(popSize)
#at this point, all candidates have an associated fitness, and we know the maxFitness candidate
for cand in population: #for every candidate normalise fitness into 10 categories
f=cand.fitness
f= (f**2)/float(maxFitness**2)
f*=20
f=int(f)
cand.set_fitness(f) #fitness is now a scale of 1 to 20, relative to fittest candidate
matePool= sampleSpace(population)
newGeneration=[]
for cand in population: #for every candidate, mate then mutate
if(random.random()<crossProb):
i=random.randint(0,len(matePool)-1) #a random index from the mating pool
mate= matePool[i]
offspring= cand.mate_crossover(mate) #mating more probable with fit candidates
newGeneration.append(offspring) #add this offspring to new generation
else:
x=generateBinrep(bitlength) #if mating fails, generate random offspring
newGeneration.append(candidate(x))
for offspring in newGeneration: #go through everyone in new generation and possibly mutate
if(random.random()<mutateProb):
offspring.mutate_switch()
print "In generation: ", genNum, " - the max fitness was: ", maxFitness
print "The max fitness candidate was: ", population[maxIndex]
print "The average fitness of the population was: ", averageFit
population= newGeneration[:] #update generation
def geneticAlgorithm(popSize, bitlength, crossProb, mutateProb, fitFunction, generations):
'''Executes a GA. First it creates a random population of size popSize, where each candidate's binrep is
specified by bitlength. Then goes through number of generations, with given evolutionary parameters'''
population= generatePop(popSize, bitlength) #Generate initial population
for genNum in range(generations): #each loop is one generation
maxFitness= 0
maxIndex= 0
totalFitness= 0
for i in range(popSize): #for every candidate evaluate fitness, keep track of fittest
f= fitFunction(population[i])
totalFitness+=f
if f>maxFitness:
maxFitness=f
maxIndex=i #note where the maxFitness was found
population[i].set_fitness(f)
averageFit= totalFitness/float(popSize)
#at this point, all candidates have an associated fitness, and we know the maxFitness candidate
newGeneration=[]
for cand in population: #for every candidate, mate then mutate
if(random.random()<crossProb):
offspring= cand.mate_crossover(population[maxIndex]) #mate with best candidate, producing offspring
newGeneration.append(offspring) #add this offspring to new generation
else:
x=generateCandidate(bitlength) #if mating fails, generate random offspring
newGeneration.append(candidate(x))
for offspring in newGeneration: #go through everyone in new generation and possibly mutate
if(random.random()<mutateProb):
offspring.mutate_switch()
print "In generation: ", genNum, " - the max fitness was: ", maxFitness
print "The max fitness candidate was: ", population[maxIndex]
print "The average fitness of the population was: ", averageFit
population= newGeneration[:] #update generation
