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Recipe: AILearnsToPlayFoldIt
Created by Grom 46 327
Your rating: None Average: 4.3 (31 votes)


Name: AILearnsToPlayFoldIt
ID: 102993
Created on: Mon, 01/14/2019 - 15:48
Updated on: Tue, 01/15/2019 - 17:25

version 1.01 It is a script that use genetic algorithm to find actions in game that gain score and adjust them over time. Source code can be found here:

Adjustable parameters: **Population Size** - Number of species in population. In other words - number of top species that survive to the next generation; **Mutation Size** - Number of mutated algorithms that will be added and checked each generation; **Aliens Size** - Number of random algorithms that will be added and checked each generation; **Cross Size** - Number of crossed algorithms that will be added and checked each generation; **Number Of Algorithm Steps** - Number of actions (genes) that can perform each algorithm; **Iteration Score Threshold** - Score gain threshold for algorithm to be executed again during evaluation; **Reset World Generation Each** - Number of generations that will be tested based on the same starting position before new top position will be saved as start point; **Mutate Rate** - Probability of new gene during mutation;

Also you can check/uncheck pre-defined algorithms to start with. By default two common algorithms included.

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Grom's picture
User offline. Last seen 17 weeks 1 day ago. Offline
Joined: 04/30/2009
Groups: Russian team
version 1.01

- Added ability to add custom pre-defined algorithm (through "More" button)
- Fixed pre-defined algorithm selection
- Added descriptions of used actions

Joined: 10/10/2015
Groups: Team China
cool, seems faster than

cool, seems faster than Rav3n's GAB (Genetic Algorithm on Bands)

Joined: 09/24/2012
Groups: Go Science

Print actions at start before the dialog would help to know what numbers of actions to select for own algorythms.

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Developed by: UW Center for Game Science, UW Institute for Protein Design, Northeastern University, Vanderbilt University Meiler Lab, UC Davis
Supported by: DARPA, NSF, NIH, HHMI, Amazon, Microsoft, Adobe, Boehringer Ingelheim, RosettaCommons