Estimation-of-distribution algorithms (EDAs) have shown strong performance in multi-valued optimization. Self-adjusting parameter control has been used in evolutionary algorithms to improve convergence speed and stability; however, to the best of our knowledge, these mechanisms have not yet been applied within the EDA framework. In this work, we introduce the self-adjusting multi-valued compact genetic algorithm (SM-cGA) and the compact genetic neuroevolution algorithm (cGNA). Both algorithms integrate success-based parameter control into the multi-valued compact genetic algorithm ( \(r\) -cGA) and neural network optimization, respectively, allowing them to automatically adjust their parameters during the search. We evaluate the SM-cGA on the G-OneMax function and the cGNA on four geometric benchmark problems. The results show that both algorithms consistently outperform static parameter settings and classical neuroevolution methods, such as the (1+1) neuroevolution algorithm with local mutation. We also provide theoretical runtime analyses of the cGNA on two geometric benchmark problems, supporting the experimental results.