Examining the effect of different configuration issues of the multiobjective evolutionary algorithms on the efficient frontier formulation for the constrained portfolio optimization problem
研究了多目标进化算法不同配置对约束投资组合优化问题有效前沿形成的影响，分析了惩罚函数、修复算子等约束处理方法的优劣，并探讨了不同遗传算子的效果。
This article examines the effect of different configuration issues of the Multiobjective Evolutionary Algorithms on the efficient frontier formulation for the constrained portfolio optimization problem. We present the most popular techniques for dealing with the complexities of the constrained portfolio optimization problem and experimentally analyse their strengths and weaknesses. In particular, we examine the efficient incorporation of complex real world constraints into the Multiobjective Evolutionary Algorithms and their corresponding effect on the efficient frontier formulation for the portfolio optimization problem. Moreover, we examine various constraint-handling approaches for the constrained portfolio optimization problem such as penalty functions and reparation operators and we draw conclusions about the efficacy of the examined approaches. We also examine the effect on the efficient frontier formulation by the application of different genetic operators and the relevant results are analysed. Finally, we address issues related with the various performance metrics that are applied for the evaluation of the derived solutions.