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)
