I tried this approach twice. Genetic tuning generated a chromosome.properties file in the data directory, which was read by the bot during a battle. It works.
The challenge is working around the noise and slowness of full battles. There are techniques for dealing with noisy and slow fitness functions, and I tried some of them.
The naive approach would be running like 15 to 50 battles per chromosome and averaging the results, taking days to evaluate a single generation.
Another approach is running a single battle for each chromosome, them averaging the results for many near chromosomes (k-NN style). Assuming similar chromosomes give similar results, averaging them can suppress the noise while still giving a meaningful fitness evaluation. It took about 1 hour per generation. The technique is called "averaging over space".
Since the fitness landscape keeps changing due to more neighbours being added all the time, another technique called "random immigrants" is useful to avoid over-fitting. The technique is basically replacing a few chromosomes (the worst ones) with new random ones.
Also tried running a single battle per chromosome without averaging, but it was too unstable. Survival of the fittest became survival of the luckiest.
Another challenge is paralellizing fitness evaluation. JGAP's built-in parallelization is awful for the techniques above, so I built another one from scratch. Pick all chromosomes from a bulk fitness function, evaluate them in parallel (RoboRunner style), regroup/average all results and return.
Tried with Combat, tuning gun/movement/energy management against DrussGT alone, but failed due to over-specialization. Also tried with Impact, tuning energy management against the Rambot Challenge 2K6, this one worked ok.
WaveSim works around the problems above by using a fixed data set and a targeting simulation.