def genetic_algorithm(args):
minmax = (args.min, args.max)
N = args.N
gens = args.gens
solution = common.initialize(N,minmax)
best_fitness = common.fitness(solution[0])
for gen in range(1, gens):
if gen % 10 == 0:
print("Generation :#%d" % gen)
mutated = mutation.ga_mutation(solution, minmax)
fitness = common.fitness(mutated[0])
if fitness <= best_fitness:
best_fitness = fitness
solution = mutated
common.write_data(gen, fitness, 'ga.dat')
if fitness == 0:
break
print("#########################")
print("# Strategy : Genetic Algorithms")
print("# Generations : " + str(gens))
print("# Best Solution Fitness : %.3f" % best_fitness)
print("# Log File : ./ga.dat")
print("# Graph : Genetic_Algorithm_Ackleys_Function.png")
print("#########################")
common.plot('Genetic Algorithm: Ackleys Function', 'ga.dat')
def genetic_algorithm(args):
minmax = (args.min, args.max)
N = args.N
gens = args.gens
solution = common.initialize(N,minmax)
best_fitness = common.fitness(solution[0])
for gen in range(1, gens):
if gen % 10 == 0:
print("Generation :#%d" % gen)
mutated = mutation.ga_mutation(solution, minmax)
fitness = common.fitness(mutated[0])
if fitness >= best_fitness:
best_fitness = fitness
solution = mutated
common.write_data(gen, fitness, 'ga.dat')
print("#########################")
print("# Strategy : Genetic Algorithms")
print("# Generations : " + str(gens))
print("# Best Solution Fitness : %.3f" % best_fitness)
print("# Log File : ./ga.dat")
print("# Graph : Genetic_Algorithm_Ackleys_Function.png")
print("#########################")
common.plot('Genetic Algorithm: Ackleys Function', 'ga.dat')
def evolutionary_strategies(args):
minmax = (args.min, args.max)
N = args.N
gens = args.gens
solution = common.initialize(N, minmax)
best_fitness = common.fitness(solution[0])
p = 1.5
for gen in range(1, gens):
if gen % 10 == 0:
print("Generation :#%d" % gen)
mutated = mutation.es_mutation(solution, minmax, p)
#print(solution[0])
#print(mutated[0])
#print(list(map(operator.sub, mutated[0], solution[0])))
fitness = common.fitness(mutated[0])
if fitness <= best_fitness:
best_fitness = fitness
solution = mutated
p = 1.5
common.write_data(gen, fitness, 'es.dat')
#elif fitness == best_fitness:
# p = 1
else:
p = 1.5**(-1 / 4)
if fitness == 0:
break
print("#########################")
print("# Strategy : Evolutionary Strategies")
print("# Generations : " + str(gens))
print("# Best Solution Fitness : %.3f" % best_fitness)
print("# Log File : ./es.dat")
print(
"# Graph : Evolutionary_Strategies_Ackleys_Function.png"
)
print("#########################")
common.plot('Evolutionary Strategies: Ackleys Function', 'es.dat')
def evolutionary_strategies(args):
minmax = (args.min, args.max)
N = args.N
gens = args.gens
solution = common.initialize(N,minmax)
best_fitness = common.fitness(solution[0])
p = 1.5
for gen in range(1, gens):
if gen % 10 == 0:
print("Generation :#%d" % gen)
mutated = mutation.es_mutation(solution, minmax, p)
#print(solution[0])
#print(mutated[0])
#print(list(map(operator.sub, mutated[0], solution[0])))
fitness = common.fitness(mutated[0])
if fitness <= best_fitness:
best_fitness = fitness
solution = mutated
p = 1.5
common.write_data(gen, fitness, 'es.dat')
#elif fitness == best_fitness:
# p = 1
else:
p = 1.5 ** (-1/4)
if fitness == 0:
break
print("#########################")
print("# Strategy : Evolutionary Strategies")
print("# Generations : " + str(gens))
print("# Best Solution Fitness : %.3f" % best_fitness)
print("# Log File : ./es.dat")
print("# Graph : Evolutionary_Strategies_Ackleys_Function.png")
print("#########################")
common.plot('Evolutionary Strategies: Ackleys Function', 'es.dat')
#!/bin/env python3
import common
# Function 2 and 3
f2 = open('f2.dat', 'w')
f3 = open('f3.dat', 'w')
for x in range(0,20000):
fitness_f2 = common.fitness(x * 0.001, 'f2')
fitness_f3 = common.fitness(x * 0.001, 'f3')
f2.write("%d, %.3f\n" % (x, fitness_f2))
f3.write("%d, %.3f\n" % (x, fitness_f3))
f2.close()
f3.close()
common.plot('F2', 'f2.dat')
common.plot('F3', 'f3.dat')
# Function 5
f5 = open('f5.dat', 'w')
for x in range(0,1000):
fitness_f5 = common.fitness(x * 0.001, 'f5')
f5.write("%d, %f \n" % (x, fitness_f5))
f5.close()
common.plot('F5', 'f5.dat')
parser = argparse.ArgumentParser()
parser.add_argument('-n', help="N value for Ackley Function",
dest='N', type=int)
parser.add_argument("--min", help="Minimal value", type=int)
parser.add_argument("--max", help="Maximum value", type=int)
parser.add_argument('-g', "--generations", help="Number of Generations",
dest='gens', type=int)
args = parser.parse_args()
minmax = (args.min, args.max)
N = args.N
gens = args.gens
solution = common.initialize(N,minmax)
best_fitness = common.fitness(solution[0])
p = 1.5
for gen in range(1, gens):
mutated = mutation.es_mutation(solution, minmax, p)
fitness = common.fitness(mutated[0])
if fitness <= best_fitness:
best_fitness = fitness
solution = mutated
p = 1.5
common.write_data(gen, fitness, 'data.dat')
else:
p = 1.5 ** (-1/4)
#!/bin/env python3
import common
# Function 2 and 3
f2 = open('f2.dat', 'w')
f3 = open('f3.dat', 'w')
for x in range(0, 20000):
fitness_f2 = common.fitness(x * 0.001, 'f2')
fitness_f3 = common.fitness(x * 0.001, 'f3')
f2.write("%d, %.3f\n" % (x, fitness_f2))
f3.write("%d, %.3f\n" % (x, fitness_f3))
f2.close()
f3.close()
common.plot('F2', 'f2.dat')
common.plot('F3', 'f3.dat')
# Function 5
f5 = open('f5.dat', 'w')
for x in range(0, 1000):
fitness_f5 = common.fitness(x * 0.001, 'f5')
f5.write("%d, %f \n" % (x, fitness_f5))
f5.close()
common.plot('F5', 'f5.dat')
