Comparative Analysis of Deep Reinforcement Learning and Evolutionary Algorithms for Optimization Problems
Abstract
Deep reinforcement learning (DRL) and evolutionary algorithms (EAs) are widely used for solving optimization problems, but their comparative effectiveness remains an open question. This paper presents an in-depth analysis of DRL and EAs in various optimization scenarios, including scheduling, logistics, and game theory. We evaluate performance based on convergence speed, solution quality, and computational resource consumption. The findings provide a comprehensive understanding of when to use DRL over EAs and vice versa, enabling better decision-making in complex optimization tasks.
References
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