THE GENETIC ALGORITHM OPTIMIZATION METHOD AND ITSAPPLICATION IN ECONOMIC ANALYSIS

This article explores the principles and optimization techniques of Genetic Algorithms (GA) and highlights their application in economic analysis and modeling. Genetic Algorithms, inspired by the natural selection process, offer a powerful method for solving complex optimization problems where traditional mathematical approaches may fall short due to non-linearity, high dimensionality, or dynamic constraints. GAs operate through key processes such as selection, crossover, and mutation, evolving solutions over successsive generations to converge on optimal or near-optimal results. The wide applicability of genetic algorithms across multiple domains - including logistics, finance, supply chain management, and macroeconomic simulation - underscores their economic relevance. In particular, the integration of GAs into economic modeling enables analysts and decision makers to address problems such as resource allocation, portfolio optimization, and market behavior analysis more effectively. The adaptability and robustness of GAs make them especially useful in handling uncertainty and complex constraint systems commonly found in real world economic environments. Furthermore, this paper presents advanced optimization techniques for enhancing GA performance, such as parameter tuning, hybridization with other algorithms, parallel computation, and domain-specific customization. Through practical examples, the article demonstrates how optimized genetic algorithms can reduce computational costs, improve model accuracy, and ultimately contribute to more a informed economic decision-making.