Saturday, March 16, 2013

Population Based Incremental Learning (PBIL)

As mentioned earlier I'm using a genetic algorithm to tune my chess evaluation module. I'm not using a classic GA where a good mother and a father is selected and their genes combined to produced the members of the next generation. I'm using a more modern approach called PBIL (Population Based Incremental Learning) and I even used it with some modifications. It is more similar now to "simulated annealing".

During my tests I made the following observations
  • The algorithm is able to converge the more important terms rather quickly
  • Terms with lesser influence converge very slowly, some none not at all given about 1000 generations
  • The choice of the fitness function is very important. Runs with some fast and simple functions failed badly.
  • The choice of the GA parameters is rather a second degree influence factor. All runs optimized the gene pool towards the fitness function very good. Some were just faster. But I haven't spent to much time trying to optimize these as a serious run with a complex fitness function takes about a week to complete. 
Here is the evolutionary progress of a run regarding the material weights. Already in generation 9 the values are very good, stable and close to their final values (except maybe for the queen weight).
Convergence data of the material values
Convergence of the Probability Vector after 9, 200, 400 and 1100 generations

As a measurement of increasing strength I checked the ability of the engines to find the best move in a set of positions. After less than 200 generations the engines reached the strength of my manual "tuned" engine. After 1000 generations the engines solved much more. Whether they also play better chess is yet to prove.


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