import numpy as np
import mlrose.mlrose as mlrose
import pandas as pd
import pickle
ps = 32
mimic_maxs = []
mimic_its = []
for i in range(5):
final_results_maxs = []
final_results_it = []
for p in range(2, ps + 1):
print(p)
fitness = mlrose.FourPeaks()
istate = np.array(np.zeros(p), dtype=int)
problem = mlrose.DiscreteOpt(length=p,
fitness_fn=fitness,
maximize=True,
max_val=2)
schedule = mlrose.ExpDecay(init_temp=0.5,
exp_const=0.005,
min_temp=0.001)
best_state, best_fitness, fitness_curve = mlrose.mimic(
problem,
max_attempts=500,
pop_size=500,
keep_pct=0.3,
import numpy as np
import mlrose.mlrose as mlrose
import pandas as pd
import pickle
ps = 32
results = []
for i in range(10, 1000, 25):
fitness = mlrose.FourPeaks(t_pct=0.15)
istate = np.array(np.zeros(ps), dtype=int)
problem = mlrose.DiscreteOpt(length=ps,
fitness_fn=fitness,
maximize=True,
max_val=2)
schedule = mlrose.ExpDecay(init_temp=0.5, exp_const=0.005, min_temp=0.001)
best_state, best_fitness, fitness_curve = mlrose.mimic(problem,
max_attempts=500,
pop_size=200,
keep_pct=0.3,
max_iters=i,
random_state=1,
curve=True)
# Define alternative N-Queens fitness function for maximization problem
# Initialize custom fitness function object
# Define initial state
print('The best state found is: ', best_state)
# import mlrose
from mlrose import mlrose
import numpy as np
import matplotlib.pyplot as plt
import time
from util import plot_fitness_curve
np.random.seed(seed=0)
# Initialize fitness function object
fitness_fn = mlrose.FourPeaks(t_pct=0.1)
state = np.random.randint(low=0, high=2, size=1000)
# Define optimization problem object
problem_fit = mlrose.DiscreteOpt(length=len(state),
fitness_fn=fitness_fn,
maximize=True,
max_val=2)
iterations = [1, 10, 100, 1000, 10000]
# ________________________________________________________________________________________________________________________
times_ga = []
fitns_ga = []
# Solve problem using the genetic algorithm
for i in iterations:
start = time.time()
best_state, best_fitness, c = mlrose.genetic_alg(problem_fit,
pop_size=200,
mutation_prob=0.1,
curve=True,
max_iters=i,