.../approach EvoQ-EA dynamically selects mutation and crossover strategies using reinforcement learning ( DQN ), optimizes exploration-exploitation balance via CMA-ES and refines search space reduction through EGT-based historical solution analysis. The framework is evaluated on 13 benchmark test functions...

...Sarit Maitra Purpose The purpose of this study is to address the challenge of optimizing joint pricing and inventory decisions in supply chains by proposing an Agentic Automation-driven Multi-Agent Reinforcement Learning ( MARL ) framework. By embedding autonomy, goal-directed behavior and self...

...Jose M.J.; Abdul Nazar K.P. Purpose This study aims to develop a novel hybrid Genetic Algorithm ( GA ) for Flexible Job-shop Scheduling Problems ( FJSP ) to improve makespan. Design/methodology/approach DIGA, a Reinforcement Learning ( RL )-driven algorithmic framework is proposed...