MATLAB Genetic Algorithm Partial Least Squares (GAPLS) Implementation

Genetic Algorithm Partial Least Squares (GA-PLS) is an optimization modeling approach that combines Genetic Algorithm (GA) and Partial Least Squares (PLS) methodologies, primarily used for solving high-dimensional data regression and variable selection problems. Implementing GA-PLS in MATLAB typically involves the following key steps: Variable Selection Optimization The genetic algorithm optimizes input variables for the PLS model by selecting optimal variable subsets to reduce redundant information and improve prediction accuracy. GA simulates natural selection and genetic mechanisms (selection, crossover, mutation) to search for the best variable combinations. In MATLAB implementation, this involves using the ga() function from the Global Optimization Toolbox with custom variable encoding schemes. PLS Modeling After variable selection, Partial Least Squares regression builds the predictive model. PLS is particularly suitable for multicollinear data, effectively extracting latent relationships between independent and dependent variables while reducing dimensionality. MATLAB's plsregress() function provides the core PLS implementation, handling cross-validation and component selection. Fitness Function Design During GA optimization, the fitness function evaluates variable subset performance based on PLS model metrics like Root Mean Square Error (RMSE) and coefficient of determination (R²). This guides the evolutionary direction. Implementation requires writing custom fitness functions that call plsregress() and compute validation statistics. MATLAB Implementation MATLAB offers the Global Optimization Toolbox for genetic algorithms and built-in PLS regression functions (plsregress), making GA-PLS implementation efficient. Users can customize fitness functions and adjust GA parameters (population size, generations, mutation rates) through optimoptions() to optimize modeling performance. GA-PLS finds widespread applications in chemometrics, bioinformatics, and engineering optimization, particularly suitable for handling high-dimensional, nonlinear datasets with complex inter-variable relationships. The method effectively balances model complexity and predictive power through evolutionary optimization.