The Performance Effect of Model Accuracy on Classification-Assisted Evolutionary Algorithms

Optimization problems with costly function evaluation widely exist in real-world applications. Surrogate models are commonly used in the field of optimization to deal with such expensive optimization problems. In surrogate model-assisted evolutionary algorithms (EAs), surrogate models like regression models, ranking models or classification models are built based on historical data and then used to compare candidate solutions in place of real function evaluations. Researchers have also proposed various methods to make better use of surrogate models in the optimization process of EAs. However, there is no comprehensive study about how much accuracy of the built model is accurate enough to bring benefits to the optimization. Motivated by this, this work proposes a method to study the performance effect of model accuracy on surrogate model-assisted EAs. Specifically, the method does not really build surrogate models but assumes different model accuracies in individual selection. Two classification-assisted EAs, classification-assisted differential evolution (CADE) and relationship classification-based preselection strategy (RCPS) are analyzed in this work. The experimental results on a set of test functions show that a weak learner with classification accuracy larger than 50% is acceptable ignoring the cost of model building. Another observation is that the performances of CADE and RCPS increase monotonically and nonlinearly with the classification accuracy.