48 lines
2.1 KiB
Python
48 lines
2.1 KiB
Python
#/bin/python
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import random
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# tasks = [4, 5, 8, 2, 10, 7]
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# tasks = [random.randint(1,20) for _ in range(100)]
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tasks = [19, 12, 12, 3, 5, 10, 2, 17, 18, 8, 20, 6, 3, 10, 6, 1, 13, 16, 17, 1, 11, 6, 12, 2, 11, 17, 8, 12, 7, 15, 1, 5, 17, 19, 5, 16, 15, 20, 7, 4, 12, 6, 17, 6, 8, 10, 8, 13, 15, 2, 6, 16, 11, 16, 16, 16, 1, 2, 17, 9, 4, 3, 7, 4, 1, 14, 16, 1, 1, 15, 20, 2, 13, 15, 15, 11, 5, 18, 1, 14, 19, 19, 19, 19, 5, 5, 12, 5, 2, 2, 2, 9, 8, 12, 6, 20, 18, 12, 12, 19]
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sol1=[1, 0, 1, 1, 0, 1]
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sol2=[0, 0, 1, 0, 1, 1]
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def generate_initial_population(problem_data, k, popsize):
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return [[random.randint(0, k-1) for _ in range(len(problem_data))] for _ in range(popsize)]
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def fitness(solution, problem_data, k):
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return max([sum(problem_data[i] for i in range(len(solution)) if solution[i] == j) for j in range(k)])
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def crossover(solutionA, solutionB):
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return [solutionA[i] if i % 2 == 0 else solutionB[i] for i in range(len(solutionA))]
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def mutation(solution, k):
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i = random.randint(0,len(solution)-1)
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solution[i] = random.choice(list({i for i in range(k)} - {solution[i]}))
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return solution
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def genetic(problem_data, k, popsize, mutation_chance, stop, reproduce):
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population = generate_initial_population(problem_data, k, popsize)
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population.sort(key = lambda x : fitness(x, problem_data, k), reverse=True)
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loop = 0
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while loop<stop:
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best = fitness(population[-1], problem_data, k)
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new = []
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for _ in range(reproduce):
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weightsP = [(i+1)**2 for i in range(popsize)]
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parents = random.choices(population, weights=weightsP, k=2)
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x = random.random()
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offspring = crossover(*parents)
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new.append(mutation(offspring, k) if mutation_chance>x else offspring)
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population = sorted(population + new, key=lambda x: fitness(x, problem_data, k), reverse=True)[-popsize:]
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new_best = fitness(population[-1], problem_data, k)
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loop = loop + 1 if best<=new_best else 0
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best = new_best
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return(population[-1])
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ans = genetic(tasks, 4, 10, 0.7, 10, 5)
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print(ans)
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print(fitness(ans, tasks, 4))
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