Adaptive Estimation of the Number of Algorithm Runs in Stochastic Optimization
Tome Eftimov, Peter Korošec · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
Determining the number of algorithm runs is a critical aspect of experimental design, as it directly influences the experiment's duration and the reliability of its outcomes. This paper introduces an empirical approach to estimating the required number of runs per problem instance for accurate estimation of the performance of the continuous single-objective stochastic optimization algorithm. The method leverages probability theory, incorporating a robustness check to identify significant imbalances in the data distribution relative to the mean, and dynamically adjusts the number of runs during execution as an online approach.