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46 | 46 | fitness = GA.cal_pop_fitness(equation_inputs, new_population)
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47 | 47 | print("Fitness")
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48 | 48 | print(fitness)
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| 49 | + |
| 50 | + best_outputs.append(numpy.max(numpy.sum(new_population*equation_inputs, axis=1))) |
| 51 | + # The best result in the current iteration. |
| 52 | + print("Best result : ", numpy.max(numpy.sum(new_population*equation_inputs, axis=1))) |
| 53 | + |
49 | 54 | # Selecting the best parents in the population for mating.
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50 | 55 | parents = GA.select_mating_pool(new_population, fitness,
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51 | 56 | num_parents_mating)
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58 | 63 | print("Crossover")
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59 | 64 | print(offspring_crossover)
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60 | 65 |
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61 |
| - # Adding some variations to the offsrping using mutation. |
| 66 | + # Adding some variations to the offspring using mutation. |
62 | 67 | offspring_mutation = GA.mutation(offspring_crossover)
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63 | 68 | print("Mutation")
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64 | 69 | print(offspring_mutation)
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67 | 72 | new_population[0:parents.shape[0], :] = parents
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68 | 73 | new_population[parents.shape[0]:, :] = offspring_mutation
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69 | 74 |
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70 |
| - best_outputs.append(numpy.max(numpy.sum(new_population*equation_inputs, axis=1))) |
71 |
| - # The best result in the current iteration. |
72 |
| - print("Best result : ", numpy.max(numpy.sum(new_population*equation_inputs, axis=1))) |
73 |
| - |
74 | 75 | # Getting the best solution after iterating finishing all generations.
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75 | 76 | #At first, the fitness is calculated for each solution in the final generation.
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76 | 77 | fitness = GA.cal_pop_fitness(equation_inputs, new_population)
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