Chess programmers and harpists

Harpists spend 90 percent of their lives tuning their harps and 10 percent playing out of tune.
Igor Stravinsky

My computer just finished a second tuning run trying to optimize the search control parameters in my engine. This time it run for another 364 hours. I optimized the parameter set a bit, removed dead parameters (parameters belonging to a feature that the first run decided to disable) and added a few new ones. I also lowered the learning rate and modified the fitness function a bit.

Due to the lower learning rate the second run converged slower but as also fewer mutations occurred the final entropy after 1.100 generations was about the same.

Convergence comparison between both runs

 To my surprise the introduction of the secondary fitness criteria lead to a parameter set that performs a very aggressive pruning. I did not expect it to have such a big impact as it is only used if the primary fitness is not decisive. The engines from the second run search a whole ply deeper and obviously took much riskier pruning decisions for that.

Average Population Search Depth for midgame positions (move 10 - 25)

The convergence towards an aggressive set also shows in the search depth the winner engine reaches in the final round of the knock out tournament in each generation.

evol-2 tournament winner depth incl. trend line

To visualize the more aggressive pruning set I publish here the safety margins for futility pruning that the final genoms converged to. Up to depth 3 the values of both runs are pretty close together. But at depth 4 to 6 evol-2 is taking much more risk than the evol-1 set.

Futility Pruning Safety Margins

Both parameters set are fairly different but their strength is not so far apart.

ELO development (engine base version = 0)

The outcome of the 2nd run seems a bit stronger. But considering the error bar they are really close together.

Rank Name            Elo    +    - games score oppo. draws
   1 evol-v2.1100     70    6    6 10004   54%    43   42%
   2 evol-v1.1100     62    6    6 10004   52%    49   41%
   3 iCE 0.4 v1604     0    7    7  6004   40%    66   40%

Maybe it is just coincidence or it is an indication how much the strength of my engine with the current code base can be improved just be tuning search parameters.

I will have to think about it and decide how to progress from here. Maybe I perform a third run to test my hypothesis.