This repository contains the experiment results and R scripts to analyze the data for the study "A Modular Hybridization of Particle Swarm Optimization andDifferential Evolution", which is accepted in The Genetic and Evolutionary Computation Conference (GECCO) '20 conference: Rick Boks, Hao Wang, and Thomas Bäck. 2020. A Modular Hybridization of Particle Swarm Optimization and Differential Evolution. In Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. ACM, New York, NY, USA, 8 pages. https: //doi.org/10.1145/3377929.3398123 Bibtex: @inproceedings{BoksWB20, author = {Rick Boks and Hao Wang and Thomas B\"ack}, title = {{A Modular Hybridization of Particle Swarm Optimization and Differential Evolution}}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2020, Canc\'un, Mexico, July 8-12, 2020}, publisher = {{ACM}}, year = {2020}, url = {https://doi.org/10.1145/3321707.3321816}, doi = {doi.org/10.1145/3377929.3398123, } Data description: we benchmarked 800 different hybridizations of the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on a well-known continuous black-box problem set called COCO/BBOB, which consists of 24 test functions. 30 independent runs are conducted for each algorithm on each problem. 'ERT.csv': a data frame with columns DIM (5D or 20D), funcId (F1-24), algId (algorithm names), target (\(10^{\{-8,-7, \ldots, 1\}}\)), ERT (expected running time), and sd (standard deviation). 'raw-data.csv': the running time recorded in each independent run. 'analysis.R': the R script that generates ERT tables in the paper. 'ecdf.R': the R script that renders the ECDF (empirical cumulative distribution function) plots in the paper.