Soly663/Genetic_Algorithm-choosingFeature

1# ga_logic.py (pseudo-code)2# This is pseudo-code written to sketch out3# the main algorithm and it's mechenism4 5 6def load_data():7    "Empty function that simulates data being loaded and returned"8    return 1, 29 10 11# Global data setup12 13X, y = load_data()14NUM_FEATURES = X.shape[1]15 16 17def calculate_fitness(chromosome):18    # Takes a binary array 'chromosome'19    # Selects features from X based on the 1s in the chromosome20    # Splits data, trains a LogisticRegression model, returns accuracy_score21    # Optional: Add a penalty for using too many features to encourage smaller solutions22    # fitness = accuracy - (num_selected_features * 0.001)23    pass24 25 26def selection(population, fitness_scores):27    # Pick the best chromosomes to be parents28    pass29 30 31def create_initial_population(size, chromosome_length):32    pass33 34 35# --- Main GA Loop ---36population = create_initial_population(size=100, chromosome_length=NUM_FEATURES)37NUM_GENERATIONS = 50038for generation in range(NUM_GENERATIONS):39    fitness_scores = [calculate_fitness(chromo) for chromo in population]40 41    # Create the next generation42    new_population = []43    for _ in range(len(population)):44 45 46    # Log the best fitness of the generation47    print(f"Generation {generation}: Best Fitness = {max(fitness_scores)}")48