Genetic Algorithm Partial Least Squares (GAPLS) Implementation in MATLAB

In this article, we explore the implementation of Genetic Algorithm Partial Least Squares (GAPLS) in MATLAB to solve specific regression problems. GAPLS combines genetic algorithm optimization with partial least squares regression to identify optimal fitting curves among multiple independent variables. This hybrid approach is particularly valuable for feature selection and model optimization in complex datasets. The GAPLS algorithm utilizes MATLAB's global optimization toolbox for genetic algorithm operations, while employing PLS regression for dimensional reduction and relationship modeling. Key implementation aspects include chromosome encoding for variable selection, fitness function calculation using PLS prediction accuracy, and genetic operators (crossover, mutation) for population evolution. In practice, GAPLS has demonstrated significant applications across various fields including chemometrics, biostatistics, and medical research. This article provides comprehensive explanations of the algorithm's mathematical foundation and practical implementation methodology, accompanied by MATLAB code examples to help users understand how to effectively apply GAPLS in their research projects. The implementation typically involves setting up population parameters, defining selection criteria, and optimizing PLS component selection through iterative genetic operations.