Genetic Algorithm Combined with PLS for Variable Selection

This program combines Genetic Algorithm with Partial Least Squares (PLS) for variable screening, addressing combinatorial optimization problems involving excessive variables. Leveraging the global search capability of Genetic Algorithms for optimization and the feature selection methodology of PLS, the program efficiently identifies optimal variable combinations to tackle challenges posed by high-dimensional data in complex problems.

The implementation utilizes GA's chromosome encoding to represent variable subsets, with fitness evaluation based on PLS model performance metrics. Through selection, crossover, and mutation operations, the algorithm iteratively refines variable combinations while maintaining population diversity. Key functions include population initialization, PLS model training with cross-validation, and elite preservation strategies to ensure convergence to near-optimal solutions.

This approach effectively reduces variable space dimensionality, enhancing model efficiency and predictive accuracy. The program incorporates visualization components to display variable selection progress, fitness evolution curves, and final solution analysis, enabling users to better understand and interpret the screening process and outcomes. Overall, it provides a robust yet straightforward solution for variable selection, offering significant practical value for researchers and practitioners working with combinatorial optimization challenges.