Model merging has emerged as a cost-effective alternative to training large language models (LLMs) from scratch, enabling researchers to combine pre-trained models into more capable systems without full retraining. Evolutionary approaches to model merging have shown particular promise, automatically searching for optimal merging configurations across both parameter space (PS) and data flow space (DFS). However, the optimization challenges underlying these approaches -- particularly in DFS merging -- remain poorly understood and formally underspecified in the literature. This paper makes two contributions. First, we provide a structured survey of evolutionary model merging techniques, organizing them into three categories: parameter-space merging, data flow space merging, and hybrid approaches. Second, we formally characterize the DFS merging problem as a black-box optimization problem involving mixed binary-continuous variables, high-dimensional search spaces, and conditional dependencies between variable types -- challenges that standard optimization methods such as CMA-ES are not designed to handle. We provide preliminary empirical validation using real pre-trained language models, demonstrating that a structured approach respecting the binary-continuous conditional dependency outperforms an unstructured approach by 6.7% accuracy while reducing the effective search space by 51.4%. By connecting the model merging community with the broader evolutionary computation and black-box optimization literature, we identify concrete open problems and propose research directions to address them.