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Question 1: Implementing a Genetic Algorithm for Feature Selection
Description:
You are tasked with implementing a genetic algorithm (GA) to perform feature selection for a classification problem. Given a dataset with n features and a target variable, design a GA to evolve a population of binary strings representing feature subsets. Evaluate the fitness of each subset using a selected classification algorithm and iteratively evolve the population through selection, crossover, and mutation operations until convergence criteria are met.
Solution Overview:
To tackle this challenge, we'll break down the implementation into several steps:
1. Initialization: Generate an initial population of binary strings representing random feature subsets.
2. Fitness Evaluation: Utilize a classification algorithm (e.g., Support Vector Machine, Random Forest) to evaluate the fitness of each feature subset based on classification accuracy.
3. Selection: Employ tournament selection to choose individuals for reproduction based on their fitness.
4. Crossover: Implement single-point crossover to create offspring from selected parents.
5. Mutation: Introduce random mutations to offspring to maintain diversity in the population.
6. Convergence: Terminate the algorithm when convergence criteria, such as maximum generations or no improvement in fitness, are met.
Python Implementation:
```python
import numpy as np
def initialize_population(pop_size, num_features):
return np.random.randint(2, size=(pop_size, num_features))
def evaluate_fitness(population, X_train, y_train):
# Use classification algorithm to evaluate fitness (e.g., accuracy)
# Return fitness scores for each individual
pass
def tournament_selection(population, fitness_scores, tournament_size):
# Select individuals based on tournament selection
pass
def single_point_crossover(parent1, parent2):
# Perform single-point crossover between two parents
pass
def mutate(offspring, mutation_rate):
# Introduce mutations in offspring based on mutation rate
pass
# Main genetic algorithm loop
population = initialize_population(pop_size, num_features)
for generation in range(max_generations):
fitness_scores = evaluate_fitness(population, X_train, y_train)
# Other GA operations (selection, crossover, mutation)
# Termination criteria check
Conclusion
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