Expensive Optimization

Expensive optimization tackles the challenge of finding optimal solutions when evaluating each solution is computationally costly. Current research focuses on improving efficiency through surrogate models (e.g., Gaussian processes, deep kernel learning) that approximate the expensive objective function, often within evolutionary algorithms or Bayesian optimization frameworks. These methods are enhanced by techniques like knowledge transfer from previous tasks and meta-learning to accelerate the search process, yielding significant improvements in solving real-world problems across diverse fields such as petroleum engineering and materials science.