A Comparative Analysis of Genetic Algorithms and NSGA-II on the Portfolio Optimisation Problem

This paper contains a comparison between a Genetic Algorithm (GA) and a Non-dominated Sorting Genetic Algorithm II (NSGA-II) on the Portfolio Optimisation Problem, based on the Modern Portfolio Theory proposed by Markowitz (1952, 1956). We use the real-world data of the S&P 500 index (quarterly returns of top 200 stocks from 2019 to 2024, and top 442 stocks from 2004 to 2016), and show that both algorithms can yield returns above the index, in mixed bull and bear market conditions. N-point Crossover accelerates algorithm convergence, and by using the Sharpe Ratio, the NSGA-II outperformed most models based on Stochastic Dominance we tested, according to the metrics in Bruni el. al. (2017).