If you actually did any thinking about the idea yourself, maybe try to explain in your own words what it's actually about, and how it builds on other papers and differs from similar techniques.
You really didn't even bother to replace [your-username] that chatgpt put in there with your actual username. And is that even your name?
@misc{genreg2024, author = {Payton Miller}, title = {GENREG: Evolutionary Neural Network Training Through Trust-Based Selection}, year = {2024}, publisher = {GitHub}, url = {https://github.com/[your-username]/genreg} }
Questions, suggestions, or collaboration inquiries: [your contact info]
Project Link: https://github.com/[your-username]/genreg
Well played.
Edit wow this some five-dimensional trolling: the github user's avatar is from the movie Idiocracy https://github.com/A1CST
AsyncVibes•1mo ago
Fitness signal stability is critical: Training plateaued at 65% with 1 random image per digit. Variance was too high. Switching to 20 images per digit fixed this immediately. Child mutation drives exploration: Mutation during reproduction matters far more than mutating existing population. Disabling it completely flatlined learning. Capacity forces trade-offs: The 32-neuron model initially masters easy digits (0, 1) then evolutionary pressure forces it to sacrifice some accuracy there to improve hard digits. Different optimization dynamic than gradient descent.
Most MNIST baselines reach 97-98% using 200K+ parameters. GENREG achieves 81% with 50K params and 72% with 25K params, showing strong parameter efficiency despite lower absolute ceiling. Other Results Alphabet recognition (A-Z): 100% mastery in ~1800 generations Currently testing generalization across 30 font variations Limitations Speed: ~40 minutes to 81% vs ~5-10 minutes for gradient descent Accuracy ceiling: Haven't beaten gradient baselines yet Scalability: Unclear how this scales to larger problems Current Experiments Architecture sweep (16/32/64/128/256 neurons) Mutation rate ablation studies Curriculum learning emergence Can we hit 90%+ on MNIST? Minimum viable capacity for digit recognition?
RaftPeople•1mo ago
I'm curious how you handled the challenges around genotype>>>phenotype mapping? For my project the neural network was fairly large and somewhat modular due to needing to support multiple different functions (i.e. vision, hearing, touch, motor, logic+control, etc.) and it felt like the problem would be too challenging to solve well (to retain general structure of network so retaining existing capabilities but also with some variation for new) so I punted and had no gene's.
I just evolved each brain based on some high level rules. The most successful creatures had a low percentage chance of changing any neuron/connection/weight/activation function/etc. and less successful creatures had a higher percentage chance of changes with the absolute worst just getting re-created entirely.
Things I noticed that I thought were interesting, wondering what things you've noticed in yours:
1-Most successful ones frequently ended up with a chokepoint, like layer 3 out of 7 where there was a smaller number of neurons and high connectivity to previous neurons.
2-Binary/step activation function ended up in successful networks much more frequently than I expected, not sure why.
3-Somewhat off topic from digit recognition but an interesting topic about ANN evolution: how to push the process forward? What conditions in the system would cause the process to find a capability that is more advanced/indirectly tied to success. For example, vision and object recognition: what is a precursor step that is valuable that the system could first develop. Also, how to create a generic environment where those things can naturally evolve without trying to steer the system.