MATLAB Implementation of Genetic Algorithm and Ant Colony Optimization for Feature Selection

The proposed methodology utilizes a hybrid approach integrating genetic algorithm with ant colony optimization for feature selection tasks. This combined technique capitalizes on the global search capabilities of genetic algorithms and the pheromone-based optimization of ant colony systems to efficiently identify relevant features in datasets. The implementation involves initializing a population of feature subsets using genetic algorithm operations (selection, crossover, mutation), then applying ant colony optimization to refine the selection through pheromone trail updates and probabilistic path selection. Key MATLAB functions employed include ga() for genetic algorithm implementation and custom pheromone matrix management for ant colony operations. The integration creates a more robust feature selection process that enhances decision-making accuracy and improves model performance across various applications by systematically exploring feature spaces while maintaining solution diversity.