knapsack_run.py

elitism = 0.1
population_size = 500
mutation_rate = 0.1
mutation_sigma = 0.1
mutation_decay = 0.999
mutation_limit = 0.01
amount_optimisation_steps = 250

sizes = [10, 5, 6, 7, 8]


for i in range(1, 6):
    print("\nproblem:", i)
    
    ga = GA(sizes[i-1],
            elitism,
            population_size,
            mutation_rate,
            mutation_sigma,
            mutation_decay,
            mutation_limit)
    
    stop = False
    while not stop:
        positions = ga.get_solutions()

        fitnesses = []
        for pos in positions:
            fitness, done = knapsack_evaluate(pos, i)
            if done:
                stop = True
                print(fitness, pos)
            fitnesses.append(fitness)

        if not stop:  
            ga.set_fitnesses(fitnesses)
            best_position, best_fitness = ga.get_best()
            
        else:
            print("stopping")

#-------------------------------------------------------
# Output:
#-------------------------------------------------------
#
# problem: 1
# 309 [1 1 1 1 0 1 0 0 0 0]
# 
# problem: 2
# 51 [0 1 1 1 0]
# 
# problem: 3
# 150 [1 1 0 0 1 0]
# 
# problem: 4
# 107 [1 0 0 1 0 0 0]
# 
# problem: 5
# 900 [1 0 1 1 1 0 1 1]