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Search-based automatic program repair has shown promise in reducing the cost of defects in real-world software. However, to date, such techniques have typically been most successful when constructing short or single-edit repairs. This is true even when techniques make use of heuristic search strategies, like genetic programming, that in principle support the construction of patches of arbitrary length. One key reason is that the fitness function traditionally depends entirely on test cases, which are poor at identifying partially correct solutions and lead to a fitness landscape with many plateaus. We propose a novel fitness function that optimizes for both functionality and semantic diversity, characterized using learned invariants over intermediate behavior. Our early results show that this new approach improves semantic diversity and fitness granularity, but does not statistically significantly improve repair performance.