11–20 of 42 posts
Remember that great old flash thingy where you mutate a boxcar until it drives well, just found out there's a html version: https://rednuht.org/genetic_cars_2/
Nueral network tech went through a weird period and then back to basics (back prop) just with massive computation (and simple activation functions) with fabulous results. I wonder if similar things could be done with other old school techniques like GA. Does anyone know if we have tried massive ones?
interesting. I did proposed a GA framework in my final thesis, but I end up never actually sharing the code, because it the end you have to create most of the code anyways. How to create your population, how to evaluate each individual, how to cross them... My final thesis is in Portuguese, otherwise I'll share a link
Do you have diagrammatic details to share ? That will be easy to understand irrespective of language.
I do intend to take the time to translate it to english.
Are genetic algorithms getting popular again? This was the topic of my senior project 20 years ago. I had barely heard about them since I left university, but all of a sudden within the last six months I'm hearing about them again.
A question for the author: is genetic data a thing? If it is what are your thoughts?
It looks usable for someone who knows about genetic algorithm. It would be catchy on your repo if you add gif version of a working demo for simple use case such as snake game or something similar.
I like the cleanliness of the library. I learned about and used GAs in college. It is important for this library to have the ability to hold out the fittest candidate so far. This avoids the situation where a good solution is lost because the population evolves away from it. Optionally, the best candidate can be retained for breeding. Deleting unfit solutions should be a parameter. Breeding partner selection should a…
I like the cleanliness of the library. I learned about and used GAs in college. It is important for this library to have the ability to hold out the fittest candidate so far. This avoids the situation where a good solution is lost because the population evolves away from it. Optionally, the best candidate can be retained for breeding. Deleting unfit solutions should be a parameter. Breeding partner selection should a…
Many frameworks also include a "hall of fame" to avoid evolving away from a good point like you mentioned.