Targeting with Genetic Algorithm
Revision as of 20:45, 2 February 2017 by Dsekercioglu (talk | contribs)
Simply it is determining the GF values in the nodes with genetic algorithm.
How To Use Genetic Algorithm In Targeting
Of course there can be lots of other ways but I will say how I used it on WormHole. WormHole improves it's gun with genetic algorithms during the match. Latest version: https://www.dropbox.com/s/gupdskse08umgtv/dsekercioglu.WormHole_1.2.jar?dl=1
- Guessing the nodes
- Make a standard segmented [GF] gun. Remove the bins so there will be only one GF value there.
- Then with genetic algorithm create some chromosomes that has their own stats.
- Keep a track of your firing data like {latVelWhenFired, advVelWhenFired, hit GF} so you will be able to use it in fitness method.
- Then sort the chromosomes and get the first chromosome in the list. Put the first chromosome's stats to your real stats.
- Mutation
- Normally mutation in genetic algorithm can hurt the genes or it can make it better.
- Generally it is randomised.
- In robocode for adapting fast it randomly takes an index (index = size - (int)Math.round(Math.random() * 10)) like this code for using valid data.
- If enemy is disabled mutation will help it to adapt quickly.
- Problems
- It skips turns if there are (too many generations per tick/ too many chromosomes)
- It can't hit strange patterns if the enemy doesn't move perpendicular to the robot.
- Note: If you have something to say just write it.