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Related Questions
- What is the primary role of surrogate models in metaheuristic optimization, and how do they aid in the search process?
- How do surrogate models balance exploration and exploitation in metaheuristic optimization, and what techniques are used to achieve this balance?
- Can you explain the concept of 'surrogate-based optimization' and how it differs from traditional metaheuristic optimization methods?
- How do surrogate models handle noisy or expensive-to-evaluate objective functions in metaheuristic optimization, and what strategies are employed to mitigate these issues?
- What are some common types of surrogate models used in metaheuristic optimization, such as polynomial chaos expansions or Gaussian processes, and how do they differ in their application?
- How do surrogate models interact with the population-based search process of metaheuristics, such as genetic algorithms or particle swarm optimization?
- Can you discuss the use of surrogate models in multi-objective optimization, and how they can be employed to handle conflicting objectives in metaheuristic optimization?
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