Feature Selection Using Genetic Algorithm and Distance Metrics

Feature selection combining genetic algorithms and distance metrics can be applied to various artificial intelligence and pattern recognition problems to achieve data dimensionality reduction and optimize model performance. Genetic algorithms simulate natural selection processes through operations like fitness evaluation, crossover, and mutation - typically implemented using population initialization, selection based on fitness scores, and genetic operators to evolve optimal feature subsets. Distance metrics serve as crucial indicators in feature selection, measuring inter-feature correlations and similarities using methods like Euclidean distance or Mahalanobis distance, thereby helping identify features with maximum discriminative power and minimum redundancy. The integration of genetic algorithms with distance-based evaluation enables more precise and efficient feature selection in AI systems, often implemented through fitness functions that combine classification accuracy with distance-based feature quality measures. This synergy enhances algorithm performance by reducing computational complexity while maintaining or improving predictive accuracy in practical applications.