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arXiv cs.LG
arXiv cs.LG
7/21/2026
Reinforcement Learning-Guided NSGA-II Enhanced with Gray Relational Coefficient for Multi-Objective Optimization: Application to NASDAQ Portfolio Optimization

Reinforcement Learning-Guided NSGA-II Enhanced with Gray Relational Coefficient for Multi-Objective Optimization: Application to NASDAQ Portfolio Optimization

Short summary

Researchers propose RL-NSGA-II-GRC, combining reinforcement learning-guided NSGA-II with gray relational coefficients for constrained multi-objective portfolio optimization. The RL agent adaptively controls evolutionary parameters while GRC-based tournament selection ranks parents by dominance, crowding, and proximity to ideal. On NASDAQ portfolio data, the method produces a smooth efficient frontier with a maximum Sharpe ratio of 1.92, outperforming standard NSGA-II by 4.4-5.8% on benchmarks.

  • •RL-NSGA-II-GRC integrates RL agent control and gray relational coefficients into NSGA-II for portfolio optimization
  • •Achieves 4.4-5.8% convergence improvement over NSGA-II on benchmark problems
  • •NASDAQ application yields efficient frontier with max Sharpe ratio of 1.92

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