Regression Analysis using Combined Genetic Algorithm and Partial Least Squares

This document presents an advanced program that integrates Genetic Algorithm (GA) and Partial Least Squares (PLS) for regression analysis. In implementation, the GA component typically functions as an optimization wrapper that selects optimal variable subsets through evolutionary operations like selection, crossover, and mutation, while PLS handles the actual regression modeling by projecting predictors and responses to latent structures. Although computationally intensive, this hybrid approach delivers superior prediction accuracy by effectively managing complex variable interactions. The genetic algorithm explores potential solution spaces through fitness-based evolution, while PLS mitigates multicollinearity effects through dimensionality reduction techniques. Key implementation aspects include chromosome encoding for feature selection, PLS component optimization via cross-validation, and fitness evaluation using prediction error metrics. Overall, this program provides robust capabilities for understanding and predicting complex datasets through synergistic algorithm combination.