GA-ELM: Genetic Algorithm Optimized Extreme Learning Machine with Data Implementation

Optimizing Extreme Learning Machines (ELM) using Genetic Algorithms significantly enhances model accuracy and stability. Genetic Algorithm (GA) is an optimization technique inspired by natural evolution processes that efficiently explores large search spaces to identify optimal solutions. In GA implementation, each potential solution represents an individual chromosome population. Through genetic operations like crossover (combining parent chromosomes) and mutation (introducing random changes), the algorithm continuously evolves toward optimal ELM parameters. Key benefits include improved prediction accuracy, enhanced model robustness, and reduced overfitting through systematic hyperparameter tuning.

Proper data handling is crucial for successful implementation. High-quality datasets with sufficient volume and broad coverage are essential for building reliable models. When implementing GA-ELM, comprehensive data preprocessing should include: data cleaning (handling missing values/outliers), feature selection (using GA for optimal feature subset identification), and dataset partitioning (train-validation-test splits). The GA optimization process typically involves encoding ELM parameters (input weights/hidden biases) as chromosomes, with fitness evaluation using cross-validation accuracy. Only with thorough data preparation and proper GA parameter configuration (population size, mutation rate, selection criteria) can the full advantages of GA-optimized ELM be realized, yielding superior predictive performance.