Parameter Identification for ARX Models Using Genetic Algorithm (GA)

The Genetic Algorithm (GA)-based parameter identification method for AutoRegressive with eXogenous input (ARX) models discussed in this paper represents a fundamental time series analysis technique. This approach employs evolutionary optimization principles where GA parameters such as population size, crossover rate, and mutation probability are systematically tuned to enhance the accuracy of ARX model parameter estimation. The implementation typically involves encoding model parameters into chromosomes and using fitness functions that minimize prediction errors through generations of selection, crossover, and mutation operations. This methodology finds applications across multiple domains including economics (for forecasting economic indicators), engineering systems (for dynamic system modeling), and natural sciences (for analyzing environmental data patterns). The technique demonstrates particular compatibility with hybrid modeling frameworks, where it can be integrated with fuzzy logic systems for handling uncertainties or combined with artificial neural networks to create neuro-genetic frameworks for improved prediction accuracy. Code implementation typically involves defining appropriate boundary constraints for parameters, designing chromosome structures that represent ARX coefficients, and establishing convergence criteria for the optimization process.