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Posted: Sun Nov 21, 2010 8:09 pm
Can you modify the spawn percentages for defilers?
Posted: Sun Nov 21, 2010 8:18 pm
Updated Zerg perc to include defilers:
Posted: Sun Nov 21, 2010 9:40 pm
Posted: Thu Nov 25, 2010 8:41 am
I'm trying to use Evolutionary Computation to optimize some values for my bot.
I have a little request. Could you help me with inserting a Trigger into the uploaded map file, so it restarts the position and health/hitpoints of all units when one of the teams has zero units left.
Of course I can just use the BWAPI.ini file to do it, but I would be faster in this way.
Posted: Fri Nov 26, 2010 4:58 am
Posted: Fri Nov 26, 2010 8:49 pm
Here's a random map I made. It's a one-time run.
You dodge yamatos by loading your transports.
No victory/loss conditions.
When the Battlecruiser starts the animation, immediately loading and unloading from a shuttle will clear the Battlecruiser's target.
In a real game the Battlecruiser will lose all its energy and the target will have been completely safe, the target will be able to move after unloading while the Battlecruiser is still in its animation. You can dodge other abilities this same way, like Broodling for example. You can also still dodge it when the bullet is moving towards the unit (works for dodging some attacks as well).
The theory was to keep a dropship/shuttle/overlord by important units so that you can dodge attacks frequently. Bunkers would also work if they are nearby (for terran organic units). Possibly an application that an AI could pull off easily.
Posted: Mon Jul 25, 2011 5:39 am
Anyone have genetic algorithm code for C++? I'd love to have a walk through for combining speed hacks, menu automation, file/database reading and writing and a simple algorithm which includes fitness functions, crossover, mutation and making new generations till a specified number of generations.
I've looked at done a bit of the speed hacks post, and will attempt menu automation as well, but the reading to and from a text file post seemed a bit too simple for something where I'm evolving populations of rules.