Abstract: Genetic code improvement systems start from an existing piece of program code and search for alternative versions with better performance according to some metric of interest. The search space of source code is a large, rough fitness landscape which can be extremely difficult to navigate. Most approaches to enhancing search capability in this domain involve either novelty search, where low-fitness areas of the search space are remembered for future avoidance, or formal analysis which attempts to find high-utility parameterizations for the GI process. In this paper we propose the use of phylogenetic analysis over genetic history to understand how different mutations and crossovers have affected the fitness of a population over time for a particular problem; we then use the results of that analysis to tune a GI process during its operation to enhance its ability to locate better program candidates. Using phylogenetic analysis on 600 runs of a genetic improver targeting a hash function, we demonstrate how the results of this analysis yield tuned mutation types over the course of a GI process (dynamically and continually set according to individual's ancestors' ranks within the population) to give hash functions with over 20\% improved fitness compared to a baseline GI process.