Optimising LMAPF guidance graphs using Evolutionary algorithms: Advice needed [R]

Hello, I'm currently working on my dissertation and feel like I could really use some advice from someone who looks at the problem with fresh eyes. I appreciate all input. The Problem: Multi Agent Path Finding is the problem of finding paths for several agents to their destinations. Lifelong MAPF is the same, but upon task completion an agent is assigned a new task. For my dissertation (and usually in research) agents move on a grid-like graph and time is discrete. Each timestep an agent can move to an adjacent tile or wait. A good LMAPF algorithm creates paths which maximise average jobs completed per timestep. Some LMAPF algorithms can also work on weighted graphs where each edge to an adjacent node (or itself) has its own cost. Such a graph is called guidance graph and the choice of edge weights can influence which paths the LMAPF algorithm creates also impacting throughput. My supervisor wanted to explore whether Evolutionary algorithms can be suitable for finding a guidance graph that improves throughput without changing the underlying LMAPF algorithm. A guidance graph is scenario specific meaning it is optimised for a specific LMAPF algorithm, map, and agent count. My algorithm so far: So far I've implemented a very basic evolutionary algorithm. An initial population of guidance graphs is randomly initialized (Limited to 10 at the moment). Then each candidate is plugged into the LMAPF algorithm for a certain amount of time steps and the completed jobs are counted to create that candidates fitness score. The top (2) candidates are selected and the rest are discarded. The top candidates are used to make a new set of candidates (no crossover). These step are repeated indefinitely. Issues I've has so far: The simulation can use a seed and is deterministic. The seed determines which nodes the jobs appear on. Using the same guidance graph but different seeds yields random fitness scores. The higher the simulation time the lower the coefficient of variation (standard