Data Sets for the study "What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms"

This is the dataset of the paper "What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms" figures.ipynb provides postprocessing codes for using the attached data to generate tables and figures in the paper. the csv folder consists of the raw data of function evaluations that algorithms used to hit each solution the first time. the metric folder consists of the processed data, which records the convergence process of two single objectives (y1 and y2), Hypervolume, and the number of obtained Pareto solutions. the gif folder consists of the gif files plotting the convergence process of solving LOTZ. The code used to generate this data is available at https://github.com/FurongYe/GSEMO