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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 pygadCopied!✕CopyStep 2: Define the Fitness Function The fitness function should evaluate how well a rule predicts outcomes. For example:
import numpy as npimport pygad# Example dataset: features and labelsX = 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 accuracyCopied!✕CopyStep 3: Configure and Run GA
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