def travelling_salesman(): N = 50 random = Random() random.setSeed(0) points = [[0 for x in xrange(2)] for x in xrange(N)] for i in range(0, len(points)): points[i][0] = random.nextDouble() points[i][1] = random.nextDouble() fill = [N] * N ranges = array('i', fill) odd_mimic = DiscreteUniformDistribution(ranges) odd = DiscretePermutationDistribution(N) ef = TravelingSalesmanRouteEvaluationFunction(points) nf = SwapNeighbor() mf = SwapMutation() cf = TravelingSalesmanCrossOver(ef) rhc_generic("TSPrhc50", ef, odd, nf, 1.0, 10000, 10, 5) sa_generic("TSPsa50", ef, odd, nf, 1.0, 10000, 10, 5, ([1E12, 1E6], [0.999, 0.99, 0.95])) ga_generic("TSPga50", ef, odd, mf, cf, 50.0, 10000, 10, 1, ([2000, 200], [0.5, 0.25], [0.25, 0.1, 0.02])) mimic_discrete("TSPmimic50", ef, odd_mimic, ranges, 300.0, 10000, 10, 1, ([200], [100], [0.1, 0.5, 0.9])) print "TSP all done"
def run_experiment(self, opName): """Run a genetic algorithms optimization experiment for a given optimization problem. Args: ef (AbstractEvaluationFunction): Evaluation function. ranges (array): Search space ranges. op (str): Name of optimization problem. """ outdir = 'results/OPT/{}'.format(opName) # get results directory outfile = 'GA_{}_{}_{}_results.csv'.format(self.p, self.ma, self.mu) fname = get_abspath(outfile, outdir) # get output filename # delete existing results file, if it already exists try: os.remove(fname) except Exception as e: print e pass with open(fname, 'w') as f: f.write('iterations,fitness,time,fevals,trial\n') # start experiment for t in range(self.numTrials): # initialize optimization problem and training functions ranges, ef = self.op.get_ef() mf = None cf = None if opName == 'TSP': mf = SwapMutation() cf = TravelingSalesmanCrossOver(ef) else: mf = DiscreteChangeOneMutation(ranges) cf = SingleCrossOver() odd = DiscreteUniformDistribution(ranges) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) ga = StandardGeneticAlgorithm(self.p, self.ma, self.mu, gap) fit = FixedIterationTrainer(ga, 10) # run experiment and train evaluation function start = time.clock() for i in range(0, self.maxIters, 10): fit.train() elapsed = time.clock() - start fe = ef.valueCallCount score = ef.value(ga.getOptimal()) ef.valueCallCount -= 1 # write results to output file s = '{},{},{},{},{}\n'.format(i + 10, score, elapsed, fe, t) with open(fname, 'a+') as f: f.write(s)
""" # set N value. This is the number of points N = 25 random = Random() points = [[0 for x in xrange(2)] for x in xrange(N)] for i in range(0, len(points)): points[i][0] = random.nextDouble() points[i][1] = random.nextDouble() ef = TravelingSalesmanRouteEvaluationFunction(points) odd = DiscretePermutationDistribution(N) nf = SwapNeighbor() mf = SwapMutation() cf = TravelingSalesmanCrossOver(ef) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) # #Try 5 rounds of RHC # expt = "expt_Restarts" # for i in range(5): # rhc = RandomizedHillClimbing(hcp) # train(rhc, "RHC", ef, 20000, "round=" + str(i), expt) # print "RHC Round" + str(i) + ": " + str(ef.value(rhc.getOptimal())) # expt = "expt_med" # # # rhc = RandomizedHillClimbing(hcp) # train(rhc, "RHC", ef, 20000, "test", expt)
def solveit(oaname, params): # set N value. This is the number of points N = 50 iterations = 1000 tryi = 1 random = Random() points = [[0 for x in xrange(2)] for x in xrange(N)] for i in range(0, len(points)): points[i][0] = random.nextDouble() points[i][1] = random.nextDouble() ef = TravelingSalesmanRouteEvaluationFunction(points) odd = DiscretePermutationDistribution(N) nf = SwapNeighbor() mf = SwapMutation() cf = TravelingSalesmanCrossOver(ef) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) if oaname == "RHC": iterations = int(params[0]) tryi = int(params[1]) oa = RandomizedHillClimbing(hcp) if oaname == "SA": oa = SimulatedAnnealing(float(params[0]), float(params[1]), hcp) if oaname == "GA": iterations=1000 oa = StandardGeneticAlgorithm(int(params[0]), int(params[1]), int(params[2]), gap) if oaname == "MMC": iterations=1000 # for mimic we use a sort encoding ef = TravelingSalesmanSortEvaluationFunction(points) fill = [N] * N ranges = array('i', fill) odd = DiscreteUniformDistribution(ranges) df = DiscreteDependencyTree(.1, ranges) pop = GenericProbabilisticOptimizationProblem(ef, odd, df) oa = MIMIC(int(params[0]), int(params[1]), pop) print "Running %s using %s for %d iterations, try %d" % (oaname, ','.join(params), iterations, tryi) print "="*20 starttime = timeit.default_timer() output = [] for i in range(iterations): oa.train() if i%10 == 0: optimal = oa.getOptimal() score = ef.value(optimal) elapsed = int(timeit.default_timer()-starttime) output.append([str(i), str(score), str(elapsed)]) print 'Inverse of Distance [score]: %.3f' % score print 'train time: %d secs' % (int(timeit.default_timer()-starttime)) scsv = 'tsp-%s-%s.csv' % (oaname, '-'.join(params)) print "Saving to %s" % (scsv), with open(scsv, 'w') as csvf: writer = csv.writer(csvf) for row in output: writer.writerow(row) print "saved." print "="*20 print "Route:" if oaname == 'MMC': optimal = oa.getOptimal() fill = [0] * optimal.size() ddata = array('d', fill) for i in range(0,len(ddata)): ddata[i] = optimal.getContinuous(i) order = ABAGAILArrays.indices(optimal.size()) ABAGAILArrays.quicksort(ddata, order) print order else: path = [] for x in range(0,N): path.append(oa.getOptimal().getDiscrete(x)) print path
def run_traveling_salesman(): # set N value. This is the number of points N = 50 random = Random() points = [[0 for x in xrange(2)] for x in xrange(N)] for i in range(0, len(points)): points[i][0] = random.nextDouble() points[i][1] = random.nextDouble() ef = TravelingSalesmanRouteEvaluationFunction(points) odd = DiscretePermutationDistribution(N) nf = SwapNeighbor() mf = SwapMutation() cf = TravelingSalesmanCrossOver(ef) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) iters = [50, 100, 250, 500, 1000, 2500, 5000, 10000, 25000, 50000, 100000] num_repeats = 5 rhc_results = [] rhc_times = [] for i in iters: print(i) for j in range(num_repeats): start = time.time() rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, i) fit.train() end = time.time() rhc_results.append(ef.value(rhc.getOptimal())) rhc_times.append(end - start) print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal())) # print "Route:" # path = [] # for x in range(0,N): # path.append(rhc.getOptimal().getDiscrete(x)) # print path sa_results = [] sa_times = [] for i in iters: print(i) for j in range(num_repeats): start = time.time() sa = SimulatedAnnealing(1E12, .999, hcp) fit = FixedIterationTrainer(sa, i) fit.train() sa_results.append(ef.value(sa.getOptimal())) sa_times.append(end - start) print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal())) # print "Route:" # path = [] # for x in range(0,N): # path.append(sa.getOptimal().getDiscrete(x)) # print path ga_results = [] ga_times = [] for i in iters: print(i) for j in range(num_repeats): start = time.time() ga = StandardGeneticAlgorithm(2000, 1500, 250, gap) fit = FixedIterationTrainer(ga, i) fit.train() end = time.time() ga_results.append(ef.value(ga.getOptimal())) print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal())) ga_times.append(end - start) # print "Route:" # path = [] # for x in range(0,N): # path.append(ga.getOptimal().getDiscrete(x)) # print path # for mimic we use a sort encoding ef = TravelingSalesmanSortEvaluationFunction(points) fill = [N] * N ranges = array('i', fill) odd = DiscreteUniformDistribution(ranges) df = DiscreteDependencyTree(.1, ranges) pop = GenericProbabilisticOptimizationProblem(ef, odd, df) mimic_results = [] mimic_times = [] for i in iters[0:6]: print(i) for j in range(num_repeats): start = time.time() mimic = MIMIC(500, 100, pop) fit = FixedIterationTrainer(mimic, i) fit.train() end = time.time() mimic_results.append(ef.value(mimic.getOptimal())) print "MIMIC Inverse of Distance: " + str( ef.value(mimic.getOptimal())) # print "Route:" # path = [] # optimal = mimic.getOptimal() # fill = [0] * optimal.size() # ddata = array('d', fill) # for i in range(0,len(ddata)): # ddata[i] = optimal.getContinuous(i) # order = ABAGAILArrays.indices(optimal.size()) # ABAGAILArrays.quicksort(ddata, order) # print order mimic_times.append(end - start) with open('travelingsalesman.csv', 'w') as csvfile: writer = csv.writer(csvfile) writer.writerow(rhc_results) writer.writerow(rhc_times) writer.writerow(sa_results) writer.writerow(sa_times) writer.writerow(ga_results) writer.writerow(ga_times) writer.writerow(mimic_results) writer.writerow(mimic_times) return rhc_results, rhc_times, sa_results, sa_times, ga_results, ga_times, mimic_results, mimic_times
def travelingsalesmanfunc(N, iterations): rhcMult = 1500 saMult = 1500 gaMult = 1 mimicMult = 3 random = Random() points = [[0 for x in xrange(2)] for x in xrange(N)] for i in range(0, len(points)): points[i][0] = random.nextDouble() points[i][1] = random.nextDouble() optimalOut = [] timeOut = [] evalsOut = [] for niter in iterations: ef = TravelingSalesmanRouteEvaluationFunction(points) odd = DiscretePermutationDistribution(N) nf = SwapNeighbor() mf = SwapMutation() cf = TravelingSalesmanCrossOver(ef) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) iterOptimalOut = [N, niter] iterTimeOut = [N, niter] iterEvals = [N, niter] start = time.time() rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, niter * rhcMult) fit.train() end = time.time() rhcOptimal = ef.value(rhc.getOptimal()) rhcTime = end - start print "RHC Inverse of Distance: optimum: " + str(rhcOptimal) print "RHC time: " + str(rhcTime) #print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal())) print "Route:" path = [] for x in range(0, N): path.append(rhc.getOptimal().getDiscrete(x)) print path iterOptimalOut.append(rhcOptimal) iterTimeOut.append(rhcTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) start = time.time() sa = SimulatedAnnealing(1E12, .999, hcp) fit = FixedIterationTrainer(sa, niter * saMult) fit.train() end = time.time() saOptimal = ef.value(sa.getOptimal()) saTime = end - start print "SA Inverse of Distance optimum: " + str(saOptimal) print "SA time: " + str(saTime) #print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal())) print "Route:" path = [] for x in range(0, N): path.append(sa.getOptimal().getDiscrete(x)) print path iterOptimalOut.append(saOptimal) iterTimeOut.append(saTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) start = time.time() ga = StandardGeneticAlgorithm(2000, 1500, 250, gap) fit = FixedIterationTrainer(ga, niter * gaMult) fit.train() end = time.time() gaOptimal = ef.value(ga.getOptimal()) gaTime = end - start print "GA Inverse of Distance optimum: " + str(gaOptimal) print "GA time: " + str(gaTime) #print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal())) print "Route:" path = [] for x in range(0, N): path.append(ga.getOptimal().getDiscrete(x)) print path iterOptimalOut.append(gaOptimal) iterTimeOut.append(gaTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) start = time.time() # for mimic we use a sort encoding ef = TravelingSalesmanSortEvaluationFunction(points) fill = [N] * N ranges = array('i', fill) odd = DiscreteUniformDistribution(ranges) df = DiscreteDependencyTree(.1, ranges) pop = GenericProbabilisticOptimizationProblem(ef, odd, df) start = time.time() mimic = MIMIC(500, 100, pop) fit = FixedIterationTrainer(mimic, niter * mimicMult) fit.train() end = time.time() mimicOptimal = ef.value(mimic.getOptimal()) mimicTime = end - start print "MIMIC Inverse of Distance optimum: " + str(mimicOptimal) print "MIMIC time: " + str(mimicTime) #print "MIMIC Inverse of Distance: " + str(ef.value(mimic.getOptimal())) print "Route:" path = [] optimal = mimic.getOptimal() fill = [0] * optimal.size() ddata = array('d', fill) for i in range(0, len(ddata)): ddata[i] = optimal.getContinuous(i) order = ABAGAILArrays.indices(optimal.size()) ABAGAILArrays.quicksort(ddata, order) print order iterOptimalOut.append(mimicOptimal) iterTimeOut.append(mimicTime) functionEvals = ef.getNumEvals() ef.zeroEvals() iterEvals.append(functionEvals) optimalOut.append(iterOptimalOut) timeOut.append(iterTimeOut) evalsOut.append(iterEvals) return [optimalOut, timeOut, evalsOut]
output_directory = "Results/Small/TSP_GA.csv" with open(output_directory, 'w') as f: f.write('iterations,fitness,time\n') for i in range(len(iteration_list)): iteration = iteration_list[i] ga_total = 0 ga_time = 0 for x in range(runs): ranges = array('i', [2] * N) fitness = TravelingSalesmanRouteEvaluationFunction(points) discrete_dist = DiscretePermutationDistribution(N) discrete_neighbor = SwapNeighbor() discrete_mutation = SwapMutation() crossover = TravelingSalesmanCrossOver(fitness) discrete_dependency = DiscreteDependencyTree(.1, ranges) genetic_problem = GGAP(fitness, discrete_dist, discrete_mutation, crossover) start = time.clock() ga_problem = StandardGeneticAlgorithm(200, 100, 10, genetic_problem) fit = FixedIterationTrainer(ga_problem, iteration) fit.train() end = time.clock() full_time = end - start ga_total += fitness.value(ga_problem.getOptimal()) ga_time += full_time ga_total_avg = ga_total / runs ga_time_avg = ga_time / runs