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洞見 - Dynamic Optimization
An Adaptive Metaheuristic Framework for Optimizing Dynamic Problems
The Adaptive Metaheuristic Framework (AMF) can intelligently adapt to changes in the optimization landscape, enabling it to maintain high-quality solutions despite frequent and unpredictable changes in problem parameters, constraints, and objectives.
Lecture Notes: A Comprehensive Introduction to Dynamic Optimization with a Focus on Upper Semi-Continuous Models and Reinforcement Learning
These lecture notes provide a rigorous yet accessible introduction to dynamic optimization, emphasizing the theoretical foundations of dynamic programming, particularly for upper semi-continuous models, and their applications in various fields, including an introduction to reinforcement learning.
