def main():
domain = [(4, 10)]*(len(all_nodes)*2+2)
# genetic alg
optimal_solution1 = genetic_alg_general.genetic_alg_general(domain, count_cross)
print optimal_solution1
draw_solution(optimal_solution1[1])
# simulated annealing
optimal_solution2 = simulated_annealing_general.simulated_annealing(domain, count_cross)
print optimal_solution2
draw_solution(optimal_solution2[0])
def main():
# random search optimization
optimal_vec = optimal_assign()
print_solution(optimal_vec)
num_slots = len(top_choices)
domain = [(0,len(game_groups)-1)]*num_slots
# ************* try genetic optimization ****************#
genetic_optimal_vec = genetic_alg_general.genetic_alg_general(domain, assign_cost)[1]
print_solution(genetic_optimal_vec)
# ************ try simulated annealing ****************#
sa_optimal_vec, optimal_cost = simulated_annealing_general.simulated_annealing(domain, assign_cost)
print optimal_cost
print_solution(sa_optimal_vec)
def main():
# random search optimization
optimal_vec = optimal_assign()
print_solution(optimal_vec)
num_slots = len(top_choices)
domain = [(0, len(game_groups) - 1)] * num_slots
# ************* try genetic optimization ****************#
genetic_optimal_vec = genetic_alg_general.genetic_alg_general(
domain, assign_cost)[1]
print_solution(genetic_optimal_vec)
# ************ try simulated annealing ****************#
sa_optimal_vec, optimal_cost = simulated_annealing_general.simulated_annealing(
domain, assign_cost)
print optimal_cost
print_solution(sa_optimal_vec)