def experiment_optimal_ga(self):
prob_length = 20
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
pop_size = 1200
rand_state = 42
max_attempt = 70
max_iter = 1000
mutation_prob = 0.1
start = time.time()
alg = GA(problem, rand_state, max_attempt, max_iter, pop_size,
mutation_prob)
best_fitness = alg.optimize()
end = time.time()
diff = abs(end - start)
print('time taken for GA- Knapsack: ' + str(diff))
def experiment_ga_1(self):
prob_lengths = np.arange(7, 30)
result = np.zeros((len(self.rand_seeds), len(prob_lengths)))
pop_size = 200
mutation_prob = 0.1
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(prob_lengths)):
prob_length = prob_lengths[j]
fl = CustomProblem(prob_length.item(), self.problem_type)
problem = fl.create_problem()
alg = GA(problem, rand_state, 10, 1000, pop_size,
mutation_prob)
best_fitness = alg.optimize()
result[i][j] = best_fitness
print(str(result))
avg_result = np.mean(result, axis=0)
print('avg result for varying input size' + str(avg_result))
title = self.problem_type + ' with GA - Input Size Variation'
plot_curve(prob_lengths, avg_result, title, 'Input Size', 'Best Score')
def experiment_ga_5(self):
mutation_probs = np.arange(0.1, 1, 0.1)
result = np.zeros((len(self.rand_seeds), len(mutation_probs)))
pop_size = 1000
max_iter = np.inf
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(mutation_probs)):
prob_length = 20
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
max_attempt = 60
mutation_prob = mutation_probs[j].item()
alg = GA(problem, rand_state, max_attempt, max_iter, pop_size,
mutation_prob)
best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('avg result ' + str(avg_result))
title = self.problem_type + ' with GA - Mutation Prob Variation'
plot_curve(mutation_probs, avg_result, title, 'Mutation Prob',
'Best Score')
def experiment_ga_4(self):
pop_sizes = np.arange(50, 1000, 100)
result = np.zeros((len(self.rand_seeds), len(pop_sizes)))
mutation_prob = 0.1
max_iter = np.inf
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(pop_sizes)):
prob_length = 20
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
max_attempt = 20
pop_size = pop_sizes[j].item()
alg = GA(problem, rand_state, max_attempt, max_iter, pop_size,
mutation_prob)
best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('avg result ' + str(avg_result))
title = self.problem_type + ' with GA - Population Size Variation'
plot_curve(pop_sizes, avg_result, title, 'Population Size',
'Best Score')
def experiment_ga_3(self):
max_iters = np.arange(500, 3000, 400)
result = np.zeros((len(self.rand_seeds), len(max_iters)))
pop_size = 200
mutation_prob = 0.1
for i in range(len(self.rand_seeds)):
rand_state = self.rand_seeds[i]
for j in range(len(max_iters)):
prob_length = 20
fl = CustomProblem(prob_length, self.problem_type)
problem = fl.create_problem()
max_attempt = 20
max_iter = max_iters[j].item()
alg = GA(problem, rand_state, max_attempt, max_iter, pop_size,
mutation_prob)
best_fitness = alg.optimize()
result[i][j] = best_fitness
avg_result = np.mean(result, axis=0)
print('avg result ' + str(avg_result))
title = self.problem_type + ' with GA - Max Iterations Variation'
plot_curve(max_iters, avg_result, title, 'Max Iterations',
'Best Score')
def experiment_ga(self):
fl = FlipFlop(7)
problem = fl.create_problem()
alg = GA(problem, 42, 10, 1000, 200, 0.1)
alg.optimize()