Feedback 06-04-2012

Work

1. Looked into sample Scala code for parameters used for the experiments of the TLS paper. Unfortunately I wasn't able to generate the same results yet. From the code, it seems following settings are used:
1. Non-Adaptive C of 0.5
2. T-test
3. minNbSamples = 25
4. minNbSamplesPerPopulation = 5
5. significanceThreshold = 0.001
2. Following 4 points are some experiments performed of Regression Trees / HOO in combination with constant / adaptive C in the Sinus experiment (averaged over 1000 tests).
3. Regression Trees with constant C (= 0.5 *  totalRewardRangeSize)











4. Regression Trees with adaptive C (= 0.5 *  parentRewardRangeSize) 











5. HOO with constant C (= 0.5 * totalRewardRangeSize) 











6. HOO with adaptive C (= 0.5 * parentRewardRangeSize) 











7. Regression trees is not able to sample at the global maximum (as of now). It does not explore and split properly to find the best region.
8. HOO does a much better job than regression trees in the sinus environment and finds the global maximum most of the time.
9. Although an adaptive c constant causes to focus more on the promising regions, it also causes the algorithm to get stuck in local maxima sometimes. Therefore, over multiple tests, the error is lower for HOO with adaptive c.