Genetic Algorithm-Based Portfolio Analysis and Optimization Using Customized Genetic Evolution

Portfolio optimization remains a critical challenge in modern investment management, particularly as traditional methods like Modern Portfolio Theory (MPT) and Value at Risk (VaR) show limitations in addressing complex, multi-dimensional investment objectives. This paper presents a novel approach to portfolio optimization using genetic algorithms (GAs), designed to overcome the constraints of conventional optimization methods while accommodating diverse investor preferences and market conditions. Our methodology implements a flexible, multi-factor optimization framework that processes multiple input parameters, including but not limited to asset prices, volatility, correlations, sustainability ratings, and regional preferences. The system employs a fitness score calculation mechanism that aggregates weighted factors across three primary categories: risk/return metrics, portfolio constraints, and asset characteristics. Through extensive validation testing across 8,000 different test cases, our GA-based approach demonstrated an average 30% improvement in portfolio fitness scores compared to initial portfolios. Notable results include successful optimization of sustainability-focused portfolios, with targeted allocations achieving 60.4% median weighting for sustainable assets when specified. The framework's ability to handle multiple objectives while maintaining computational efficiency (8–10 second response time) represents a significant advancement in personalized portfolio management. Our findings (accuracy of 91%, AUC score of 92%, and Log loss of 0.27) suggest that genetic algorithms offer a more robust and adaptable approach to portfolio optimization compared to traditional methods, particularly in addressing the growing demand for personalized investment solutions and specialized investment criteria such as ESG considerations.