Least Squares Support Vector Machine Optimized with Genetic Algorithm

The Genetic Algorithm-optimized Least Squares Support Vector Machine (GA-LSSVM) is an intelligent optimization method that combines evolutionary computation with traditional supervised learning, applicable to both regression and classification tasks. The core innovation lies in leveraging genetic algorithms' global search capability to automatically optimize key SVM parameters (such as kernel type and penalty coefficient), effectively avoiding local optima traps common in traditional grid search methods.

The genetic algorithm simulates biological evolution mechanisms (selection, crossover, mutation) to iteratively generate parameter combinations, using the cross-validation accuracy of the LSSVM model as the fitness evaluation criterion in each iteration. Compared to standard SVM, the least squares approach transforms quadratic programming problems into linear equation systems through equality constraints, significantly reducing computational complexity. This enables the combined method to maintain efficiency even on industrial-scale datasets.

A key advantage is the modular code design – the genetic algorithm and SVM modules remain independent, facilitating portability across different hardware platforms and integration into existing machine learning pipelines. Users only need to define parameter search ranges, and the system automatically outputs optimized hyperparameters and corresponding models, making it particularly suitable for applications lacking tuning expertise.

Typical applications include financial time-series prediction (with parameters adapting to non-stationary data) and medical diagnosis (automatically selecting optimal kernel functions). Future enhancements could incorporate parallel computing to accelerate the evolutionary process, or introduce multi-objective optimization to simultaneously balance model accuracy and sparsity.