Applications of multi-objective evolutionary algorithms in economics and finance: A survey

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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): “[13], Ponsich et al.”
NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (2018, methodology): “…Sorting : The core component of NSGA-Net is the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) Deb et al. (2000), a multi-objective optimization algorithm that has been successfully employed for solving a variety of multiobjective problems Tapia & Coello (2007); Pedersen & Yang (2006).”
A review of multi-objective optimization: Methods and its applications (2018, methodology): “In the field of finance (Horn, Nafpliotis, & Goldberg, 1994; Ruspini & Zwir, 1999; Tapia & Coello, 2007; Zwir & Ruspini, 1999), to identify significant patterns of technical analysis in the financial time series, the niched-Pareto genetic algorithm (NPGA) is used.”