If you could automate the search for something in GOL, what would it be?
Posted: December 15th, 2022, 3:44 pm
I'm building a tool to search over 64x64 GOL scenarios, and I'm looking for ideas about what to search for! What would you like to see?
I'm experimenting with a novel kind of genetic algorithm, and I'm using the Game of Life as its sandbox. I released a proof of concept, with some pretty basic fitness goals. It produced some cool and mesmerizing simulations, but I presume nothing particularly useful or notable to the GOL community. For round 2, I hope to make the search process much more versatile and powerful, and I'd love some more challenging goals to match.
But what to search for? I'm very curious what you think. Are there any classes of pattern that aren't thoroughly explored, or where some AI support / competition would be welcome? Any thoughts on what qualities make a GOL simulation interesting, exciting, or delightful? Any open theoretical questions that could be answered with a "proof by example"? Something totally different? If you have ideas, or advice on where to find them, I'd love to hear from you.
Thanks!
-- Nate Gaylinn
I'm experimenting with a novel kind of genetic algorithm, and I'm using the Game of Life as its sandbox. I released a proof of concept, with some pretty basic fitness goals. It produced some cool and mesmerizing simulations, but I presume nothing particularly useful or notable to the GOL community. For round 2, I hope to make the search process much more versatile and powerful, and I'd love some more challenging goals to match.
But what to search for? I'm very curious what you think. Are there any classes of pattern that aren't thoroughly explored, or where some AI support / competition would be welcome? Any thoughts on what qualities make a GOL simulation interesting, exciting, or delightful? Any open theoretical questions that could be answered with a "proof by example"? Something totally different? If you have ideas, or advice on where to find them, I'd love to hear from you.
Thanks!
-- Nate Gaylinn