Journal of Data Processing Hu Xu, Karen Petrie 15 2 2025 https://doi.org/10.6025/jdp/2025/15/2/51-58 https://www.dline.info/jdp/fulltext/v15n2/jdpv15n2_1.pdf This paper explores an automated approach to tuning constraint solvers using genetic algorithms (GAs). In traditional constraint programming, selecting preprocessing parameters is a manual process that requires expertise, creating a barrier for novices. The authors propose a self-learning genetic algorithm (SLGA) that leverages knowledge from minor problem instances to guide the search for optimal preprocessing in larger instances. SLGA begins by solving small-scale constraint satisfaction problems to identify effective preprocessing strategies. These strategies then form the starting population for the GA when applied to larger problems, replacing the standard random initialization. Two strategies are tested: Learning from Best (LFB) and Learning from Genetic (LFG), with LFB utilising the best-known methods and LFG employing those derived from previous GA runs. Experiments on benchmark problems (BIBD, N-Queen, Golomb, and Langford's number) show that SLGA outperforms standard GAs in both efficiency and solution quality. Particularly, LFG consistently finds better preprocessing settings, even when exhaustive search is infeasible. The paper concludes that SLGA is a promising tool for automating configuration in constraint solving, and future work will explore its application to more complex and large-scale problems.