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Performance metrics, similar framework, Framework, State of the Art, To be…
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Framework
Dynamic Switching
State management: ensure that the state of the computation can be saved, transferred and restored when switching algorithms
cost-benefit analysis: what are the potential gains in performance, to avoid inefficient switching
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Decision making
Implement a decision engine, such as RL or decision theory to choose the best algorithm dynamically
State of the Art
Algorithm Performance Metrics: Look at how current research measures algorithm performance beyond the traditional metrics of speed and accuracy. Consider metrics like energy efficiency or adaptability.
Adaptive and Dynamic Systems: Study systems that adapt to changing conditions in real-time, particularly how they manage state and decision-making processes.
Reinforcement Learning and Meta-Learning: Explore how these areas are being developed to improve algorithm selection and system adaptability.
Sustainability: Investigate how computational sustainability is being addressed in current research, particularly in how algorithm choices can reduce environmental impacts.
To be considered
Green Computing Practices: Research how your framework can contribute to green computing initiatives by optimizing energy use and reducing the environmental impact of large-scale computations.
Meta-Learning: Applying meta-learning can help the system generalize the performance patterns of algorithms across similar types of problems or datasets, improving its predictive accuracy over time.
Hybrid Models: Consider using hybrid approaches that combine RL with other machine learning techniques like supervised learning for better prediction accuracy or unsupervised learning for discovering new patterns or features that affect algorithm performance.
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possible applications
data sorting
You could investigate how your framework chooses and switches between different sorting algorithms (like quicksort, mergesort, heapsort) based on the size and nature of the data sets.
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