# Results of grid-search for $d=3$ ![](https://i.imgur.com/xd7P5hP.png) The best set of parameters seem to be pop_size=100, n_gen=100, ngames=100, eps=0.1 ![](https://i.imgur.com/DPFfoom.png) Note that MWPM is beaten consistently for some specific set of parameters! The curves roughly stay with the same ordering against the error rate, this motivates the visualization at a specific error rate (taken to be 0.1 in the following). ## Influence of the population size ![](https://i.imgur.com/P5brCv5.png) **Comments** - Increasing the population size is always beneficial (except this one case with low epsilon) ## Influence of the number of generations ![](https://i.imgur.com/LbM5E3T.png) **Comments** - In most cases, increasing the number of generations from 100 to 200 increase marginally the efficiency of the correction - For two cases, it seems to make things worse - **100 generations seem sufficient to get a solution** ## Influence of the number of training puzzles ![](https://i.imgur.com/pVAtI5D.png) **Comments:** - There is no clear trend, analyze better? ## Influence of epsilon $\epsilon$ ![](https://i.imgur.com/OH0qOQe.png) **Comments** - not so clear # Results of grid-search for $d=5$ ![](https://i.imgur.com/6Gegovt.png)