This is the result of the study "Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance." We compare grid search with three automated algorithm configuration methods, iterated racing (Irace), mixed-integer parallel efficient global optimization (MIP-EGO), and mixed-integer evolutionary strategies (MIES). The genetic algorithm (GA) is tuned for better the expected running time (ERT) and the area under the empirical cumulative distribution function curve (AUC). The result is tested on 25 pseudo-boolean problems. This Data set consists of 3 parts: 1. data and configurations: The performance of the configured GAs obtained by the configurators on 25 pseudo-Boolean problems defined in IOHprofiler (https://iohprofiler.github.io/), and the parameter settings of the GAs. 2. pvalues-le and pvalues-ge: The p-values of using Wilcoxon–Mann–Whitney two-sample rank-sum test to compare the runtimes to hit the target of the obtained GAs to the (1+1)-EA. The alternative hypothesis is that the evaluation times of (1+1)-EA is less than the evaluation times of the obtained GAs for pvalues-le.csv, and the alternative hypothesis is that the evaluation times of (1+1)-EA is greater than the evaluation times of the obtained GAs for pvalues-ge.csv, 3. Irace-OM and Irace-LO: The result of 20 Irace runs on Onemax and LeadingOnes. The algorithms are named as 'Irace-cost metric-Budget ratio t-ID of runs'. The cost metric is ERT or AUC. The maximal evaluation budget of each configuration is \((0.5+0.1t) \times ERT_{(1+1)-EA}, t \in [0..15]\). Contact: if you have any questions or suggestions, please feel free to contact Furong Ye.