遗传算法：原理及 Python 实现

import numpy as np

# Define the fitness function
def fitness(x):
  # Maximize the function f(x) = x^2
  return x**2
1.
2.
3.
4.
5.
6.

# Define the GA parameters
POP_SIZE = 100
GENS = 100
CROSSOVER_PROB = 0.8
MUTATION_PROB = 0.2
1.
2.
3.
4.
5.

# Initialize the population
pop = np.random.rand(POP_SIZE)

# Evaluate the fitness of the initial population
fitness_values = np.array([fitness(x) for x in pop])
1.
2.
3.
4.
5.

parents = np.array([pop[np.argmax(fitness_values)] for _ in range(POP_SIZE//2)])
1.

offspring = []
  for _ in range(POP_SIZE//2):
    parent1, parent2 = parents[np.random.randint(0, len(parents), 2)]
    child = (parent1 + parent2) / 2
    offspring.append(child)
1.
2.
3.
4.
5.

for i in range(len(offspring)):  # Iterate over the correct range of offspring
    if np.random.rand() < MUTATION_PROB:
      offspring[i] += np.random.normal(0, 0.1)
1.
2.
3.

import numpy as np

# Define the fitness function
def fitness(x):
  # Maximize the function f(x) = x^2
  return x**2

# Define the GA parameters
POP_SIZE = 100
GENS = 100
CROSSOVER_PROB = 0.8
MUTATION_PROB = 0.2

# Initialize the population
pop = np.random.rand(POP_SIZE)

# Evaluate the fitness of the initial population
fitness_values = np.array([fitness(x) for x in pop])

# Main GA loop
for gen in range(GENS):

  # Selection
  parents = np.array([pop[np.argmax(fitness_values)] for _ in range(POP_SIZE//2)])
  
  # Crossover
  offspring = []
  for _ in range(POP_SIZE//2):
    parent1, parent2 = parents[np.random.randint(0, len(parents), 2)]
    child = (parent1 + parent2) / 2
    offspring.append(child)
  
  # Mutation
  for i in range(len(offspring)):  # Iterate over the correct range of offspring
    if np.random.rand() < MUTATION_PROB:
      offspring[i] += np.random.normal(0, 0.1)
  
  # Replace the population with the new offspring
  pop = offspring
  
  # Evaluate the fitness of the new population
  fitness_values = np.array([fitness(x) for x in pop])
  
  # Print the best fitness value
  print(f"Generation {gen+1}, Best Fitness: {np.max(fitness_values)}")
# Print the final best solution
print(f"Final Best Solution: {pop[np.argmax(fitness_values)]}")
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.

Generation 1, Best Fitness: 1.4650152220573687
Generation 2, Best Fitness: 1.8054426063247935
Generation 3, Best Fitness: 2.1124584418178354
Generation 4, Best Fitness: 2.34514080269685
.          
.
.
.
.
Generation 99, Best Fitness: 254.58556629300833
Generation 100, Best Fitness: 260.9705918019082
Final Best Solution: 16.154584234882314
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from deap import base, creator, tools, algorithms

# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Define the number of features to select
num_features = 3

# Define the fitness function
def fitness(individual):

    # Select the features based on the individual
    selected_indices = [i for i, x in enumerate(individual) if x == 1]
    
    # Handle the case where no features are selected
    if not selected_indices:
        return 0,  # Return a low fitness value if no features are selected
    selected_features = np.array([X[:, i] for i in selected_indices]).T
    
    # Create a random forest classifier with the selected features
    clf = RandomForestClassifier(n_estimators=100)
    
    # Evaluate the model using cross-validation
    scores = cross_val_score(clf, selected_features, y, cv=5)
    
    # Return the mean score as the fitness value
    return np.mean(scores),

# Create a DEAP creator for the fitness function
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

# Create a DEAP toolbox for the GA
toolbox = base.Toolbox()
toolbox.register("attr_bool", np.random.choice, [0, 1])
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=len(X[0]))
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("evaluate", fitness)

# Create a population of 50 individuals
pop = toolbox.population(n=50)

# Evaluate the initial population
fitnesses = toolbox.map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
    ind.fitness.values = fit

# Run the GA for 20 generations
for g in range(20):
    offspring = algorithms.varAnd(pop, toolbox, cxpb=0.5, mutpb=0.1)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit
    pop = toolbox.select(offspring, k=len(pop))

# Print the best individual and the corresponding fitness value
best_individual = tools.selBest(pop, k=1)[0]
print("Best Individual:", best_individual)
print("Best Fitness:", best_individual.fitness.values[0])

# Select the features based on the best individual
selected_features = np.array([X[:, i] for i, x in enumerate(best_individual) if x == 1]).T

# Print the selected features
print("Selected Features:", selected_features)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.

Best Individual: [0, 0, 1, 1]
Best Fitness: 0.9666666666666668
Selected Features: [[1.4 0.2]
                    [1.4 0.2]
                      .
                      .
                      .
                    [5.1 1.8]]
1.
2.
3.
4.
5.
6.
7.
8.