New adaptive algorithm enhances evolutionary search with diverse surrogate models

Researchers have developed a new adaptive surrogate-assisted evolutionary algorithm (SAEA) that improves prediction quality and robustness by constructing ensemble models. This algorithm optimizes the structure of radial basis function networks (RBFNs) by minimizing approximation error and model complexity, leading to more accurate surrogate models with varying degrees of smoothness. An infill criterion was also designed to help prescreen solutions from these diverse surrogate models. Experiments showed this approach outperformed state-of-the-art SAEAs on benchmark and real-world problems. AI

IMPACT Enhances optimization capabilities for complex problems by improving surrogate model accuracy and robustness.

RANK_REASON The cluster contains a research paper detailing a novel algorithm for evolutionary computation. [lever_c_demoted from research: ic=1 ai=1.0]

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