This is the experimental result of the study "Benchmarking a (μ+λ) Genetic Algorithm with Configurable Crossover Probability". A novel (μ+λ) GA is proposed and benchmarked, in which we stochastically determine whether to apply the crossover operator either for each individual or generation with a crossover probability \(p_c\). This data set consists of two parts: The results of (μ+λ) GA on 25 pseudo-Boolean problems defined in IOHprofiler (https://iohprofiler.github.io/) with the following setup: \(\mu \in \{10, 50, 100\}, \lambda \in \{1, \lceil\mu/2\rceil, \mu\}, p_c\in\{0, 0.5\}.\) 'IOHprofiler_Problems_standard_bit_mutation.csv' --> the (μ+λ) GA with standard bit mutation. 'IOHprofiler_Problems_fast_mutation.csv' --> the (μ+λ) GA with fast mutation. The results of (μ+λ) GA on OneMax and LeadingOnes problems with the following setup: \(n \in \{64,100,150,200,250,500\}, \mu \in \{2,3,5,8,10,20,30,...,100\}, \\ \lambda \in \{1, \lceil \mu/2 \rceil, \mu\}, \text{and }p_c \in \{0.1 k \mid k \in [0..9]\}\cup\{0.95\}.\) 'OneMax_raw.csv' --> the fixed-target running time/first hitting time from 100 independent runs for target values in \([1..n]\). 'OneMax_summary.csv' --> the mean, median, standard deviation, some quantiles, expected running time (ERT), the number of successful runs, and the success rate from 100 independent runs for target values in \([1..n]\). 'LeadingOnes_raw.csv' --> the same with 'OneMax_raw.csv' for LeadingOnes. 'LeadingOnes_summary.csv' --> the same with 'OneMax_summary.csv' for LeadingOnes. Contact: if you have any questions or suggestions, please feel free to contact Furong Ye or Carola Doerr.