def main():
target_string = "Hello World!"
population_size = 1400
runs = 10
"""
The main method for the application. The Genetic Algorithm uses tournament
selection as selection method and k-point or one-point crossover as
crossover methods. The Genetic Algorithm constructor takes in the following parameters:
@param: target_string The target string
@param: population_size Amount of randomly generated strings
@param: crossover_rate Crossover Rate in percentage (i.e. 1 = 100%)
@param: mutation_rate Mutation Rate in percentage
@param: is_k_point_crossover Choose whether to choose k-point crossover as
crossover method. If false, one-point crossover is performed
@param: tournament_size_percent Percentage of population to participate in
tournaments for selection
@param: strongest_winner_probability Probability of strongest participant
in tournament to win, as well as the second strongest's probability
"""
ga = GeneticAlgorithm(target_string, population_size, 0.8, 0.05, True,
0.05, 0.65)
ga.set_show_each_chromosome(False)
ga.set_show_crossover_internals(False)
ga.set_show_mutation_internals(False)
ga.set_silent(
False) # If False it shows the fittest chromosome of each generation
ga.run(runs)
ga.get_stats()
"""
The Hill Climbing constructor takes in the following parameters:
@param: target_string The target string
@param: solutions_size Amount of solutions (strings) to search for a
better solution in
"""
hc = HillClimbing(target_string, population_size)
hc.set_show_each_solution(False)
hc.set_silent(True)
hc.run(runs)
hc.get_stats()
"""
The Random Search constructor takes in the following parameters:
@param: target_string The target string
@param: solutions_size Amount of solutions (strings) generated randomly
each round in which the solution is searched for
"""
rs = RandomSearch(target_string, population_size)
rs.set_show_each_solution(False)
rs.set_silent(True)
rs.run(runs)
rs.get_stats()
def main():
runs = 10
rounds = 5
chromosome_size = 23
population_size = 1000
data_set_name = 'bigfaultmatrix.txt'
pwd = os.path.abspath(os.path.dirname(__file__))
data_set_path = os.path.join(pwd, data_set_name)
parser = CSVParser(data_set_path)
test_case_fault_matrix = parser.parse_data(True)
ga = GeneticAlgorithm(test_case_fault_matrix, chromosome_size,
population_size, rounds, 0.8, 0.08, 0.05, 0.75)
ga.set_show_each_chromosome(False)
ga.set_show_fitness_internals(False)
ga.set_show_crossover_internals(False)
ga.set_show_mutation_internals(False)
ga.set_show_duplicate_internals(False)
ga.set_silent(True)
ga.run(runs)
ga_fitness = ga.get_stats()
for i in range(0, 2):
if i == 0:
hc = HillClimbing(test_case_fault_matrix, chromosome_size,
population_size, rounds, False)
else:
hc = HillClimbing(test_case_fault_matrix, chromosome_size,
population_size, rounds, True)
hc.set_show_each_solution(False)
hc.set_show_fitness_internals(False)
hc.set_show_swapping_internals(False)
hc.set_silent(True)
hc.run(runs)
if i == 0:
hc_internal_fitness = hc.get_stats()
else:
hc_external_fitness = hc.get_stats()
rs = RandomSearch(test_case_fault_matrix, chromosome_size, population_size,
rounds)
rs.set_show_each_solution(False)
rs.set_silent(True)
rs.run(runs)
rs_fitness = rs.get_stats()
rs_data = np.array(rs_fitness)
hs_internal = np.array(hc_internal_fitness)
hs_external = np.array(hc_external_fitness)
ga_data = np.array(ga_fitness)
# test_cases_per_test_suite = np.array([5, 10, 20, 23, 30, 50, 100])
# unique_large_apfd = np.array([0.4594736842105263, 0.6063157894736844, 0.6867105263157895, 0.6978260869565216, 0.7128947368421051, 0.7326842105263159, 0.7480263157894737])
# full_large_apfd = np.array([0.44631578947368417, 0.6023684210526316, 0.6846052631578947, 0.6958810068649884, 0.7122807017543858, 0.7320526315789474, 0.7476578947368421])
# plt.plot(test_cases_per_test_suite, unique_large_apfd, '-gD')
# plt.xlabel("Test Cases per Test Suite")
# plt.ylabel("Mean Fitness (APFD)")
# plt.xticks(np.arange(min(test_cases_per_test_suite), max(test_cases_per_test_suite) + 1, 5.0))
# combine these different collections into a list
data_to_plot = [rs_data, hs_internal, hs_external, ga_data]
# Create a figure instance
fig = plt.figure(1, figsize=(9, 6))
# Create an axes instance
ax = fig.add_subplot(111)
# add patch_artist=True option to ax.boxplot()
bp = ax.boxplot(data_to_plot, patch_artist=True)
# change outline color, fill color and linewidth of the boxes
for box in bp['boxes']:
# change outline color
box.set(color='#7570b3', linewidth=2)
# change fill color
box.set(facecolor='#1b9e77')
# change color and linewidth of the whiskers
for whisker in bp['whiskers']:
whisker.set(color='#7570b3', linewidth=2)
# change color and linewidth of the caps
for cap in bp['caps']:
cap.set(color='#7570b3', linewidth=2)
# change color and linewidth of the medians
for median in bp['medians']:
median.set(color='#b2df8a', linewidth=2)
# change the style of fliers and their fill
for flier in bp['fliers']:
flier.set(marker='o', color='#e7298a', alpha=0.5)
# Custom x-axis labels
ax.set_xticklabels([
'Random Search', 'HC Internal Swap', 'HC External Swap',
'Genetic Algorithm'
])
# Remove top axes and right axes ticks
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# Save the figure
graph_path = os.path.join(pwd, 'graph.pdf')
pdf = PdfPages(graph_path)
plt.savefig(pdf, format='pdf', bbox_inches='tight')
plt.show()
pdf.close()
