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GeneticAnnealing.py
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import GeneticFunctions
import AnnealingFunctions
import random
class GeneticAnnealing(object):
def __init__(self,population_size,problem_size,pcross,pmutate,temp_max,temp_change,GA_iterations,SA_iterations,k,KnapsackObj):
self.population_size = population_size
self.problem_size = problem_size
self.pcross = pcross
self.pmutate = pmutate
self.temp_max = temp_max
self.temp_change = temp_change
self.GA_iterations = GA_iterations
self.SA_iterations = SA_iterations
self.k = k
self.GA = GeneticFunctions.GeneticFunctions(KnapsackObj)
self.SA = AnnealingFunctions.AnnealingFunctions(KnapsackObj)
if self.population_size%2!=0:
self.population_size += 1
def run_igsa(self):
#Initialize population
population = self.GA.initializePopulation(self.population_size,self.problem_size)
evaluations = self.GA.evaluatePopulation(population)
bestever = population[self.GA.getBest(evaluations)]
currentbest = bestever
x = [0]
y = [self.GA.evaluateIndividual(currentbest)]
temp = self.temp_max
for i in range(self.GA_iterations):
parents = self.GA.getParents(population,evaluations,self.k)
children = []
for p in range(0,len(parents),2):
parent1 = parents[p]
parent2 = parents[p+1]
child1,child2 = parent1,parent2
#crossover
if random.uniform(0,1)<self.pcross:
child1,child2 = self.GA.crossover(parent1,parent2)
#mutate
if random.uniform(0,1)<self.pmutate:
child1 = self.GA.mutate(child1)
if random.uniform(0,1)<self.pmutate:
child2 = self.GA.mutate(child2)
children.append(child1)
children.append(child2)
#Replace population with next children
evaluations = self.GA.evaluatePopulation(children)
population = children
#Send fittest indiviual to SA
bestindex = self.GA.getBest(evaluations)
currentbest = population[bestindex]
for j in range(self.SA_iterations):
neigbourSolution = self.SA.getNeighbouringSolution(currentbest)
temp = self.SA.getTemperature(i,temp,self.temp_change)
siCost = self.SA.getValue(neigbourSolution)
sCost = self.SA.getValue(currentbest)
if siCost <= sCost:
currentbest = neigbourSolution
if siCost <= self.SA.getValue(bestever):
bestever = neigbourSolution
elif self.SA.getMonteCarlo(sCost,siCost,temp) > random.uniform(0,1):
currentbest = neigbourSolution
population[bestindex] = currentbest
x.append(i+1)
y.append(self.GA.evaluateIndividual(population[bestindex]))
return bestever,x,y