KW-Race and Fast KW-Race: Racing-Based Frameworks for Tuning Parameters of Evolutionary Algorithms on Black-Box Optimization Problems

Setting proper parameters is vital for using Evolutionary Algorithms (EAs) to optimize problems, while parameter tuning is a time-consuming task. Previous approaches focus on tuning parameter configurations that are suitable for multiple problems or problem instances. However, according to the No Free Lunch (NFL) theorem, there is no generic parameter configuration that is fit for all problems. Moreover, practitioners are usually concerned with their particular optimization problem at hand and desire to obtain an acceptable result with less computational cost. Therefore, in this paper, the KW-Race framework is first proposed for solving the parameter tuning task of EAs on certain black-box optimization problem. Then a measure of convergence speed is embedded in the preceding framework to form the Fast KW-Race (F-KW-Race) framework for further reducing the computational cost of the tuning procedure. Experimental studies illustrate remarkable results and further demonstrate the validity and efficiency of the proposed frameworks.