Symbolic Regression-Assisted Offline Data-Driven Evolutionary Computation

When solving optimization problems with expensive or implicit objective functions, evolutionary algorithms (EAs) commonly utilize surrogate models as cost-effective substitutes for evaluation. This category of algorithms is referred to as data-driven EAs (DDEAs). However, when constructing surrogate models, existing studies rely on the hand-crafted model structure, requiring prior knowledge while leading to the suboptimal fitting ability of the model. To address the issue, this article proposes a novel symbolic regression (SR)-assisted EA, namely SR-DDEA. SR-DDEA employs SR to automatically construct the model structure without prior knowledge and obtain accurate surrogates. Specifically, we develop an efficient gene expression programming algorithm to enhance the expressive ability of surrogates, assisted by a queue-based decoding strategy to improve the efficiency of the model calculations. We also employ a clustering-based selective ensemble method to maximize data utilization and obtain diverse models. Experimental findings on commonly employed benchmarks demonstrate that our algorithm surpasses other cutting-edge offline DDEAs on test problems of different scales and a practical aerodynamic airfoil design challenge.