Anti-Surfer Targeting

2G of data takes me 5s (4 threads in parallel), which is 1NN with less than 5 attributes which should be lightning fast anyway.

Using all the waves (including virtual ones) and use maxK=100 with a 10+ attributes huge tree takes me less than a minute (still 4 threads in parallel).

I'm using NIO for file reading, and I use handmade serialization instead of the java builtin one, which the secret to speed.

5 seconds?? I just started using 4 threads and it takes 11 seconds with 1.4 MB's of data without virtual waves, max K 100 and 102 population size.

What is your fitness function? Mine perfectly simulates WhiteFang's targeting including bot width calculations. I don't think the 51 bin system slows down the robot since it should just be faster as long as I have K more than 51.

I convert all the data into ArrayLists so file reading speed shouldn't affect much(Or the memory it takes slows it down?).

It's 1NN with only firing waves. It seems that kd-tree is the only slow part.

Worth mention that I already store everything slow to file, e.g. precise intersection, precise mea etc. So all I do is load those attributes, transform with my formula, load into tree and do kde for every firing wave.

Anyway this can be considered as 1 population and 1 generation, as I'm tuning it by hand yet.

I don’t use GUI. The recorded files are located in each robocode installation, inside regular data file location.

I’m trying GA then. I’m tired tuning anti-random gun by hand, because tuning for one set of data decreases performance against another set of data (1500 battles should be enough, but it’s not the case when all your improvements are below 1% hit rate)

I have even tried sorting by "last modified" and "last created" but nothing seems to have appeared. I am still getting data files with the development version though. Can't test the packed one because of the bug in 1.9.3.5.

Edit: .data was a hidden directory. command + shift + . solved all the problems.

Update: after some profiling, it confirmed that kd-tree is the only bottleneck.

However, it seems that file reading time grows as kd-tree time grows.

And after putting deserializaion into separate thread and use some producer-consumer pattern to communicate, total run time stays the same and file reading time decreased greatly. Maybe my profiling tool is yielding inaccurate result.