A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications

By Antonin Ponsich, Antonio López Jaimes, Carlos Artemio Coello Coello. 2013

342 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Mathematical Optimization, Economics & Operations Research, Evolutionary Computation

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “Later, Multiobjective Evolutionary Algorithms (MOEAs) were extensively surveyed on various financial applications including financial time series prediction [11, 12, 13].”
Deep Learning for Financial Applications : A Survey (2020, neutral): “[14], Aguilar-Rivera et al.”
An easy-to-use real-world multi-objective optimization problem suite (2020, methodology): “On the one hand, as mentioned above, EMOAs have been applied to a number of real-world problems, including supersonic wing design problems [16], ﬁnance/economics application problems [17], oil production planning problems [18], and electric vehicle control problems [19].”
An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, background): “For example, the portfolio optimization problem is to maximize return and minimize risk [2], the next release problem is to maximize customer satisfaction and minimize cost [3], and the community detection problem is to maximize intralink density and minimize interlink density [4].”
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, background): “MULTIOBJECTIVE optimization problems (MOPs) are commonly seen in real-world applications, especially in the areas of engineering, biology, and economics [1]–[5].”
AI in Finance: Challenges, Techniques, and Opportunities (2021, methodology): “Further, computational intelligence techniques and methods inspired by nature and human systems and simulating natural and human intelligence are used to model, characterize, and optimize EcoFin mechanisms, strategies, and systems, and to manage and optimize portfolio and market forecasting, and so ”
A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, background): “In the real world, many optimization problems may involve multiple conflicting objectives to be optimized simultaneously [10, 11, 14, 29, 30, 46, 49].”