def experiment_rhc_1(self): print('restart vary') init_state = None restart_lengths = np.arange(10, 800, 100) result = np.zeros((len(self.rand_seeds), len(restart_lengths))) #best_state = np.zeros((len(self.rand_seeds), len(restart_lengths))) print(self.problem_type) for i in range(len(self.rand_seeds)): rand_state = self.rand_seeds[i] prob_length = 20 for j in range(len(restart_lengths)): restart_length = restart_lengths[j] max_iter = np.inf #max_attempts is varied by trial and error max_attempts = 100 fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() alg = RHC(problem, init_state, rand_state, max_attempts, max_iter, restart_length.item()) best_state, best_fitness = alg.optimize() result[i][j] = best_fitness print('best fitness') print(str(result)) print('best state') print(best_state) avg_result = np.mean(result, axis=0) print('avg result for varying input size' + str(avg_result)) title = self.problem_type + ' with RHC - # of Restarts Variation' plot_curve(restart_lengths, avg_result, title, '# of Restarts', 'Best Score')
def experiment_sa_7(self): init_state = None schedule_var = 2 best_state = None max_iters = np.arange(100, 5000, 100) result = np.zeros((len(self.rand_seeds), len(max_iters))) 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 = 200 max_iter = max_iters[j].item() alg = SA(problem, init_state, rand_state, schedule_var, max_attempt, max_iter) best_state, best_fitness = alg.optimize() result[i][j] = best_fitness avg_result = np.mean(result, axis=0) print('best_state') print(best_state) print('avg result ' + str(avg_result)) title = self.problem_type + ' with SA - Max Iter Variation - Arith' plot_curve(max_iters, avg_result, title, 'Max Iterations', 'Best Score')
def experiment_sa_4(self): init_state = None schedule_var = 1 best_state = None max_attempts = np.array([5, 10, 15, 40, 60, 80, 100, 150, 200]) result = np.zeros((len(self.rand_seeds), len(max_attempts))) for i in range(len(self.rand_seeds)): rand_state = self.rand_seeds[i] for j in range(len(max_attempts)): prob_length = 20 max_iter = np.inf fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() max_attempt = max_attempts[j].item() alg = SA(problem, init_state, rand_state, schedule_var, max_attempt, max_iter) best_state, best_fitness = alg.optimize() result[i][j] = best_fitness avg_result = np.mean(result, axis=0) print('avg result ' + str(avg_result)) print('best_state') print(best_state) title = self.problem_type + ' with SA - Max Attempts Variation -Geom' plot_curve(max_attempts, avg_result, title, 'Max Attempts', 'Best Score')
def experiment_sa_11(self): init_state = None prob_lengths = np.arange(7, 30) schedule_var = 0 best_state = None result = np.zeros((len(self.rand_seeds), len(prob_lengths))) best_state = None 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 = SA(problem, init_state, rand_state, schedule_var, 10, 1000) best_state, best_fitness = alg.optimize() result[i][j] = best_fitness print(str(result)) print('best_state') print(best_state) avg_result = np.mean(result, axis=0) print('avg result for varying input size' + str(avg_result)) title = self.problem_type + ' with SA - Input Size Variation' plot_curve(prob_lengths, avg_result, title, 'Input Size', 'Best Score')
def experiment_rhc_3(self): init_state = None max_iters = np.arange(100, 5000, 100) result = np.zeros((len(self.rand_seeds), len(max_iters))) 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 = 200 restarts = 150 max_iter = max_iters[j].item() alg = RHC(problem, init_state, rand_state, max_attempt, max_iter, restarts) best_score, best_fitness = alg.optimize() result[i][j] = best_fitness avg_result = np.mean(result, axis=0) print('avg result ' + str(avg_result)) print('best score') print(best_score) title = self.problem_type + ' with RHC - Max Iterations Variation' plot_curve(max_iters, avg_result, title, 'Max Iterations', 'Best Score')
def experiment_rhc_22(self): init_state = None max_attempts = np.array( [5, 10, 15, 30, 40, 50, 60, 80, 100, 200, 300, 350]) result = np.zeros((len(self.rand_seeds), len(max_attempts))) best_score = None for i in range(len(self.rand_seeds)): restarts = 0 rand_state = self.rand_seeds[i] for j in range(len(max_attempts)): prob_length = 20 fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() max_attempt = max_attempts[j].item() max_iter = np.inf alg = RHC(problem, init_state, rand_state, max_attempt, max_iter, restarts) best_score, best_fitness = alg.optimize() result[i][j] = best_fitness avg_result = np.mean(result, axis=0) print('avg result ' + str(avg_result)) print('best score') print(best_score) title = self.problem_type + ' with RHC - Max Attempts Variation - 0 restart' plot_curve(max_attempts, avg_result, title, 'Max Attempts', 'Best Score')
def experiment_optimal_sa(self): prob_length = 20 init_state = None schedule_var = 0 rand_state = 42 max_attempt = 110 fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() start = time.time() alg = SA(problem, init_state, rand_state, schedule_var, max_attempt, 1000) best_score, best_fitness = alg.optimize() end = time.time() diff = abs(end - start) print('time taken for SA - Knapsack: ' + str(diff))
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_optimal_rhc(self): prob_length = 20 max_attempt = 100 restarts = 100 max_iter = 1000 rand_state = 42 init_state = None fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() start = time.time() alg = RHC(problem, init_state, rand_state, max_attempt, max_iter, restarts) best_score, best_fitness = alg.optimize() end = time.time() diff = abs(end - start) print('time taken for RHC- Knapsack: ' + str(diff))
def experiment_optimal_mimic(self): prob_length = 20 fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() pop_size = 800 rand_state = 42 max_attempt = 100 max_iter = 1000 keep_pct = 0.1 start = time.time() alg = Mimic(problem, rand_state, max_attempt, max_iter, pop_size, keep_pct) best_fitness = alg.optimize() end = time.time() diff = abs(end - start) print('time taken for Mimic - Sixpeaks: ' + str(diff))
def experiment_mimic_1(self): prob_lengths = np.arange(7, 30) result = np.zeros((len(self.rand_seeds), len(prob_lengths))) pop_size = 200 keep_pct = 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 = Mimic(problem, rand_state, 10, 1000, pop_size, keep_pct) 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 mimic - Input Size Variation' plot_curve(prob_lengths, avg_result, title, 'Input Size', 'Best Score')
def experiment_mimic_5(self): keep_pcts = np.arange(0.1, 1, 0.1) result = np.zeros((len(self.rand_seeds), len(keep_pcts))) pop_size = 800 max_iter = np.inf for i in range(len(self.rand_seeds)): rand_state = self.rand_seeds[i] for j in range(len(keep_pcts)): prob_length = 20 fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() max_attempt = 200 keep_pct = keep_pcts[j].item() alg = Mimic(problem, rand_state, max_attempt, max_iter, pop_size, keep_pct) 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 Mimic - Keep PCT Variation' plot_curve(keep_pcts, avg_result, title, 'Keep PCT', 'Best Score')
def experiment_mimic_4(self): pop_sizes = np.arange(200, 1000, 200) result = np.zeros((len(self.rand_seeds), len(pop_sizes))) max_iter = np.inf keep_pct = 0.1 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 = 200 pop_size = pop_sizes[j].item() alg = Mimic(problem, rand_state, max_attempt, max_iter, pop_size, keep_pct) 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 Mimic - Population Size Variation' plot_curve(pop_sizes, avg_result, title, 'Population Size', 'Best Score')
def experiment_mimic_3(self): max_iters = np.arange(1000, 5000, 100) result = np.zeros((len(self.rand_seeds), len(max_iters))) pop_size = 800 keep_pct = 0.6 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 = 200 max_iter = max_iters[j].item() alg = Mimic(problem, rand_state, max_attempt, max_iter, pop_size, keep_pct) 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 Mimic - Max Iterations Variation' plot_curve(max_iters, avg_result, title, 'Max Iterations', '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_2(self): max_attempts = np.array([5, 10, 15, 30, 40, 50, 60, 80, 100]) result = np.zeros((len(self.rand_seeds), len(max_attempts))) 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_attempts)): prob_length = 20 fl = CustomProblem(prob_length, self.problem_type) problem = fl.create_problem() max_attempt = max_attempts[j].item() max_iter = np.inf 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 Attempts Variation' plot_curve(max_attempts, avg_result, title, 'Max Attempts', 'Best Score')