The insights from this work on software configuration tuning can be applied to other domains that involve optimizing complex systems with expensive evaluations and rugged landscapes in the following ways:
Parameter Tuning in Machine Learning: The techniques developed for software configuration tuning can be adapted for hyperparameter optimization in machine learning models. By treating hyperparameters as configuration options and performance metrics as objectives, we can apply similar optimization strategies to find optimal hyperparameter settings.
Resource Allocation in Cloud Computing: Optimizing resource allocation in cloud computing environments involves configuring various parameters to achieve desired performance metrics. By applying adaptive weight mechanisms and partial duplicate retention strategies, we can improve the efficiency of resource allocation algorithms and escape local optima in the allocation process.
Supply Chain Optimization: In supply chain management, optimizing complex supply chain networks involves tuning various parameters to maximize efficiency and minimize costs. By leveraging multi-objective optimization techniques and dynamic weight adaptation, we can enhance decision-making processes in supply chain optimization and navigate rugged landscapes more effectively.