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  1. Genetic Algorithms (GAs) can be applied to rule discovery by evolving candidate rules over generations to maximize a fitness function that measures their predictive accuracy or coverage. In Python, libraries like PyGAD make this process easier by handling selection, crossover, and mutation automatically.

    Using PyGAD for Rule Discovery

    Step 1: Install PyGAD

    pip install pygad
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    Step 2: Define the Fitness Function The fitness function should evaluate how well a rule predicts outcomes. For example:

    import numpy as np
    import pygad

    # Example dataset: features and labels
    X = np.array([[1,0,1],[0,1,0],[1,1,1],[0,0,1]])
    y = np.array([1,0,1,0])

    def fitness_func(ga_instance, solution, solution_idx):
    predictions = (np.dot(X, solution) > 1).astype(int)
    accuracy = np.mean(predictions == y)
    return accuracy
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    Step 3: Configure and Run GA

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