import numpy as np
import random
from math import ceil
from pyDOE import lhs
[docs]def create_new_individuals(design, problem, pop_size=None):
"""Create new individuals to the population.
The individuals can be created randomly, by LHS design, or can be passed by the
user.
Design does not apply in case of EvoNN and EvoDN2 problem, where neural networks
are created as individuals.
Parameters
----------
design : str, optional
Describe the method of creation of new individuals.
"RandomDesign" creates individuals randomly.
"LHSDesign" creates individuals using Latin hypercube sampling.
"EvoNN" creates Artificial Neural Networks as individuals.
"EvoDN2" creates Deep Neural Networks.
problem : baseProblem
An object of the class Problem
pop_size : int, optional
Number of individuals in the population. If none, some default population
size based on number of objectives is chosen.
Returns
-------
individuals : list
A list of individuals.
"""
if pop_size is None:
pop_size_options = [50, 105, 120, 126, 132, 112, 156, 90, 275]
pop_size = pop_size_options[problem.num_of_objectives - 2]
if design == "RandomDesign":
lower_limits = np.asarray(problem.get_variable_lower_bounds())
upper_limits = np.asarray(problem.get_variable_upper_bounds())
individuals = np.random.random((pop_size, problem.n_of_variables))
# Scaling
individuals = individuals * (upper_limits - lower_limits) + lower_limits
return individuals
elif design == "LHSDesign":
lower_limits = np.asarray(problem.get_variable_lower_bounds())
upper_limits = np.asarray(problem.get_variable_upper_bounds())
individuals = lhs(problem.n_of_variables, samples=pop_size)
# Scaling
individuals = individuals * (upper_limits - lower_limits) + lower_limits
return individuals
elif design == "EvoNN":
"""Create a population of neural networks for the EvoNN algorithm.
Individuals are 2d arrays representing the weight matrices of the NNs.
One extra row is added for bias.
"""
w_low = problem.params["w_low"]
w_high = problem.params["w_high"]
in_nodes = problem.num_of_variables
num_nodes = problem.params["num_nodes"]
prob_omit = problem.params["prob_omit"]
individuals = np.random.uniform(
w_low, w_high, size=(pop_size, in_nodes, num_nodes)
)
# Randomly set some weights to zero
zeros = np.random.choice(
np.arange(individuals.size), ceil(individuals.size * prob_omit)
)
individuals.ravel()[zeros] = 0
# Set bias
individuals = np.insert(individuals, 0, 1, axis=1)
return individuals
elif design == "EvoDN2":
"""Create a population of deep neural networks (DNNs) for the EvoDN2 algorithm.
Each individual is a list of subnets, and each subnet contains a random amount
of layers and
nodes per layer. The subnets are evolved via evolutionary algorithms, and they
converge
on the final linear layer of the DNN.
"""
individuals = []
for i in range(problem.params["pop_size"]):
nets = []
for j in range(problem.params["num_subnets"]):
layers = []
num_layers = np.random.randint(1, problem.params["max_layers"])
in_nodes = len(problem.subsets[j])
for k in range(num_layers):
out_nodes = random.randint(2, problem.params["max_nodes"])
net = np.random.uniform(
problem.params["w_low"],
problem.params["w_high"],
size=(in_nodes, out_nodes),
)
# Randomly set some weights to zero
zeros = np.random.choice(
np.arange(net.size),
ceil(net.size * problem.params["prob_omit"]),
)
net.ravel()[zeros] = 0
# Add bias
net = np.insert(net, 0, 1, axis=0)
in_nodes = out_nodes
layers.append(net)
nets.append(layers)
individuals.append(nets)
return individuals
elif design == "BioGP":
return problem.create_individuals()
