def ex2(maximize): black_box = (lambda a, b, c, d, e, f, g, h, i, j, k:( abs(a * b + a * c + a * d + a * e + a * f + a * g + a * h + a * i + a * j + b * c + b * d + b * e + b * f + b * g + b * h + b * i + b * j + c * d + c * e + c * f + c * g + c * h + c * i + c * j + d * e + d * f + d * g + d * h + d * i + d * j + e * f + e * g + e * h + e * i + e * j + f * g + f * h + f * i + f * j + g * h + g * i + g * j + h * i + h * j + i * j + a * k + b * k + c * k + d * k + e * k + f * k + g * k + h * k + i * k + j * k))) target_fitness = None variables = 11 carry_over = 10 params = { 'objective_function': black_box, 'iterations': iterations, 'mutation_probability': mutation_probability, "crossover_rate": crossover_rate, "constraint_range": range(-1000, 1000), "number_of_variables": variables, "carry_over": carry_over, "pool_size": initial_pool, "target": target_fitness, "max": maximize, "function_name": "ex2 Max: {a}".format(a=maximize) } genetic_algorithms_py.__init__(params)
def ex8(maximize): black_box = (lambda a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q:( abs(a * b + a * c + a * d + a * e + a * f + a * g + a *h + a * i + a * j + b * c + b * d + b * e + b * f + b *g + b * h + b * i + b * j + c * d + c * e + c * f + c *g + c * h + c * i + c * j + d * e + d * f + d * g + d *h + d * i + d * j + e * f + e * g + e * h + e * i + e *j + f * g + f * h + f * i + f * j + g * h + g * i + g *j + h * i + h * j + i * j + a * k + b * k + c * k + d *k + e * k + f * k + g * k + h * k + i * k + j * k + a *l + b * l + c * l + d * l + e * l + f * l + g * l + h *l + i * l + j * l + k * l + a * m + b * m + c * m + d *m + e * m + f * m + g * m + h * m + i * m + j * m + k *m + l * m + a * n + b * n + c * n + d * n + e * n + f *n + g * n + h * n + i * n + j * n + k * n + l * n + m *n + a * o + b * o + c * o + d * o + e * o + f * o + g *o + h * o + i * o + j * o + k * o + l * o + m * o + n *o + a * p + b * p + c * p + d * p + e * p + f * p + g *p + h * p + i * p + j * p + k * p + l * p + m * p + n *p + o * p + a * q + b * q + c * q + d * q + e * q + f *q + g * q + h * q + i * q + j * q + k * q + l * q + m *q + n * q + o * q + p * q))) target_fitness = None variables = 17 carry_over = 10 params = { 'objective_function': black_box, 'iterations': iterations, 'mutation_probability': mutation_probability, "crossover_rate": crossover_rate, "constraint_range": range(-1000, 1000), "number_of_variables": variables, "carry_over": carry_over, "pool_size": initial_pool, "target": target_fitness, "max": maximize, "function_name": "ex8 Max: {a}".format(a=maximize) } genetic_algorithms_py.__init__(params)
def himmelblau(maximize): print 'Blackbox: Himmelblau function' black_box = (lambda x, y: (((x ** 2) + y - 11) ** 2) + ((x + (y ** 2) - 7) ** 2)) target_fitness = None variables = 2 carry_over = 64 params = { 'objective_function': black_box, 'iterations': iterations, 'mutation_probability': mutation_probability, "crossover_rate": crossover_rate, "constraint_range": range(-50,2000), "number_of_variables": variables, "carry_over": carry_over, "pool_size": initial_pool, "target": target_fitness, "max": maximize, "function_name": "Himmelblau Function maximized: {a}".format(a=maximize) } genetic_algorithms_py.__init__(params)
def rosenbrock(maximize): print 'Blackbox: Rosenbrock function' black_box = (lambda x, y: (reduce( (lambda p, q: q + (100 * ((x - (y ** 2) ) ** 2) + (1 - y) ** 2)), range(1,2), 0))) target_fitness = None variables = 2 carry_over = 64 params = { 'objective_function': black_box, 'iterations': iterations, 'mutation_probability': mutation_probability, "crossover_rate": crossover_rate, "constraint_range": range(-450,450), "number_of_variables": variables, "carry_over": carry_over, "pool_size": initial_pool, "target": target_fitness, "max": maximize, "function_name": "Rosenbrock Function maximized: {a}".format(a=maximize) } genetic_algorithms_py.__init__(params)
def dejong(maximize): print 'Blackbox: deJongSphere function' black_box = (lambda x: (reduce( (lambda r, q: q + (x ** 2)), range(1, 5), 0))) target_fitness = None variables = 1 carry_over = 64 params = { 'objective_function': black_box, 'iterations': iterations, 'mutation_probability': mutation_probability, "crossover_rate": crossover_rate, "constraint_range": range(-6, 6), "number_of_variables": variables, "carry_over": carry_over, "pool_size": initial_pool, "target": target_fitness, "max": maximize, "function_name": "deJong Sphere maximized: {a}".format(a=maximize) } genetic_algorithms_py.__init__(params)
def test_it_should_return(): assert isinstance(genetic_algorithms_py.__init__(params), list)
def ex16(maximize): black_box = (lambda a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y:( abs(a * b + a * c + a * d + a * e + a * f + a * g + a * h + a * i + a * j + b * c + b * d + b * e + b * f + b * g + b * h + b * i + b * j + c * d + c * e + c * f + c * g + c * h + c * i + c * j + d * e + d * f + d * g + d * h + d * i + d * j + e * f + e * g + e * h + e * i + e * j + f * g + f * h + f * i + f * j + g * h + g * i + g * j + h * i + h * j + i * j + a * k + b * k + c * k + d * k + e * k + f * k + g * k + h * k + i * k + j * k + a * l + b * l + c * l + d * l + e * l + f * l + g * l + h * l + i * l + j * l + k * l + a * m + b * m + c * m + d * m + e * m + f * m + g * m + h * m + i * m + j * m + k * m + l * m + a * n + b * n + c * n + d * n + e * n + f * n + g * n + h * n + i * n + j * n + k * n + l * n + m * n + a * o + b * o + c * o + d * o + e * o + f * o + g * o + h * o + i * o + j * o + k * o + l * o + m * o + n * o + a * p + b * p + c * p + d * p + e * p + f * p + g * p + h * p + i * p + j * p + k * p + l * p + m * p + n * p + o * p + a * q + b * q + c * q + d * q + e * q + f * q + g * q + h * q + i * q + j * q + k * q + l * q + m * q + n * q + o * q + p * q + c * v + d * v + e * t + f * t + g * t + h * t + i * t + j * t + k * t + l * t + m * t + n * t + o * t + p * t + q * t + r * t + s * t + a * u + b * u + c * u + d * u + e * u + f * u + g * u + h * u + i * u + j * u + k * u + l * u + m * u + n * u + o * u + p * u + q * u + r * u + s * u + t * u + a * v + b * v + a * r + b * r + c * r + d * r + e * r + f * r + g * r + h * r + i * r + j * r + k * r + l * r + m * r + n * r + o * r + p * r + q * r + a * s + b * s + c * s + d * s + e * s + f * s + g * s + h * s + i * s + j * s + k * s + l * s + m * s + n * s + o * s + p * s + q * s + r * s + a * t + b * t + c * t + d * t + e * v + f * v + g * v + h * v + i * v + j * v + k * v + l * v + m * v + n * v + o * v + p * v + q * v + r * v + s * v + t * v + u * v + a * w + b * w + c * w + d * w + e * w + f * w + g * w + h * w + i * w + j * w + k * w + l * w + m * w + n * w + o * w + p * w + q * w + r * w + s * w + t * w + u * w + v * w + a * x + b * x + c * x + d * x + e * x + f * x + g * x + h * x + i * x + j * x + k * x + l * x + m * x + n * x + o * x + p * x + q * x + r * x + s * x + t * x + u * x + v * x + w * x + a * y + b * y + c * y + d * y + e * y + f * y + g * y + h * y + i * y + j * y + k * y + l * y + m * y + n * y + o * y + p * y + q * y + r * y + s * y + t * y + u * y + v * y + w * y + x * y))) target_fitness = None variables = 25 carry_over = 10 params = { 'objective_function': black_box, 'iterations': iterations, 'mutation_probability': mutation_probability, "crossover_rate": crossover_rate, "constraint_range": range(-10, 100000), "number_of_variables": variables, "carry_over": carry_over, "pool_size": initial_pool, "target": target_fitness, "max": maximize, "function_name": "ex16 Max: {a}".format(a=maximize) } genetic_algorithms_py.__init__(params)
def ex12(maximize): black_box = (lambda a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u:( abs(a * b + a * c + a * d + a * e + a * f + a * g + a * h + a * i + a * j + b * c + b * d + b * e + b * f + b * g + b * h + b * i + b * j + c * d + c * e + c * f + c * g + c * h + c * i + c * j + d * e + d * f + d * g + d * h + d * i + d * j + e * f + e * g + e * h + e * i + e * j + f * g + f * h + f * i + f * j + g * h + g * i + g * j + h * i + h * j + i * j + a * k + b * k + c * k + d * k + e * k + f * k + g * k + h * k + i * k + j * k + a * l + b * l + c * l + d * l + e * l + f * l + g * l + h * l + i * l + j * l + k * l + a * m + b * m + c * m + d * m + e * m + f * m + g * m + h * m + i * m + j * m + k * m + l * m + a * n + b * n + c * n + d * n + e * n + f * n + g * n + h * n + i * n + j * n + k * n + l * n + m * n + a * o + b * o + c * o + d * o + e * o + f * o + g * o + h * o + i * o + j * o + k * o + l * o + m * o + n * o + a * p + b * p + c * p + d * p + e * p + f * p + g * p + h * p + i * p + j * p + k * p + l * p + m * p + n * p + o * p + a * q + b * q + c * q + d * q + e * q + f * q + g * q + h * q + i * q + j * q + k * q + l * q + m * q + n * q + o * q + p * q + e * t + f * t + g * t + h * t + i * t + j * t + k * t + l * t + m * t + n * t + o * t + p * t + q * t + r * t + s * t + a * u + b * u + c * u + d * u + e * u + f * u + g * u + h * u + i * u + j * u + k * u + l * u + m * u + n * u + o * u + p * u + q * u + r * u + s * u + t * u + a * r + b * r + c * r + d * r + e * r + f * r + g * r + h * r + i * r + j * r + k * r + l * r + m * r + n * r + o * r + p * r + q * r + a * s + b * s + c * s + d * s + e * s + f * s + g * s + h * s + i * s + j * s + k * s + l * s + m * s + n * s + o * s + p * s + q * s + r * s + a * t + b * t + c * t + d * t))) target_fitness = None variables = 21 carry_over = 10 params = { 'objective_function': black_box, 'iterations': iterations, 'mutation_probability': mutation_probability, "crossover_rate": crossover_rate, "constraint_range": range(-1000, 1000), "number_of_variables": variables, "carry_over": carry_over, "pool_size": initial_pool, "target": target_fitness, "max": maximize, "function_name": "ex12 Max: {a}".format(a=maximize) } genetic_algorithms_py.__init__(params)