Genetic Algorithm for Feature Selection in Pattern Classification

Genetic Algorithm serves as an effective feature selection method for pattern classification. It enables optimal feature subset selection from datasets like MNIST to enhance classification performance. This section details the implementation workflow of genetic algorithms for feature selection, including key components such as population initialization, fitness function design (typically using classification accuracy as the evaluation metric), crossover operations (e.g., single-point crossover), mutation mechanisms, and selection strategies (like roulette wheel selection). The process involves encoding features as binary chromosomes, where each gene represents the inclusion (1) or exclusion (0) of a specific feature. Practical MATLAB/Python code segments will demonstrate chromosome evaluation through k-fold cross-validation and iterative optimization until convergence criteria are met. This approach helps readers understand both the theoretical foundation and hands-on implementation of evolutionary computation in feature dimensionality reduction.