MATLAB Implementation of Genetic Algorithm for Support Vector Machine Parameter Optimization

This article explores the implementation of a genetic algorithm to optimize kernel function parameters and other relevant hyperparameters for Support Vector Machines (SVMs). The primary objective of this algorithm is to systematically experiment with and adjust parameter combinations to enhance SVM performance and accuracy. Genetic algorithms are computational methods inspired by natural selection and evolutionary principles, simulating genetic variation and selection processes observed in biological evolution. When applied to SVM parameter optimization, this approach enables better understanding and control of model complexity while achieving superior prediction outcomes.

Specifically, the genetic algorithm implementation begins with an initial population of parameter sets (chromosomes) that undergo fitness evaluation. The evaluation process involves training and testing SVM models using these parameters and calculating performance metrics such as accuracy and error rates through cross-validation. Based on selection criteria (e.g., tournament or roulette wheel selection), high-fitness individuals undergo crossover (using techniques like single-point or uniform crossover) and mutation operations (applying small random perturbations to parameters) to generate new candidate solutions. This iterative process continues until meeting termination conditions such as maximum generations or convergence thresholds.

In summary, this algorithm provides an effective methodology for optimizing SVM parameters to improve model performance and predictive accuracy. Through genetic algorithm implementation in MATLAB, researchers can systematically explore parameter spaces, understand SVM model complexities, and identify optimal parameter configurations for enhanced data classification and prediction tasks.