Paper Title
Portfolio Optimisation using an Improved Genetic Algorithm in an Uncertain Environment

Abstract
This paper introduces an improved genetic algorithm (GA) called a fuzzy vector evaluated GA (VEGA), which can be used in portfolio optimisation problems in uncertain environments. The absolute deviation of the zigzag uncertain variable is preferred as a risk measure over traditional risk measures to develop a computationally more feasible model. To demonstrate the importance and efficacy of the proposed model, a comprehensive comparison is made with a widely used GA, namely the multi-objective GA (MOGA). To make our work more realistic and relevant to today’s financial market while also eliminating any bias, the latest data set is extracted from NSE India’s website and from different sectors. The results show that the improved GA outperforms the widely used GA in almost all cases, thus helping investors select optimal portfolios.