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Comment by verdverm

5 days ago

> by people who had no idea what they were doing.

I would personally hold this opinion of anyone still using genetic/evolutionary inspired algorithms for symbolic regression. There are multiple deterministic algorithms that do not have the same inherent drawbacks and are generally quite good.

Before I wrote my algo, and was trying to scale out GP, I hit the limits of RNG and had to go find some with seriously huge cycle properties. (howany calls before they reached the end and started over producing the same sequence)

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verdverm

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archermarks  4 days ago

Can you share more? I've only heard of genetic-based symbolic regression

  • verdverm  4 days ago

    I believe I wrote the second deterministic algorithm for SR

    - FFX (Fast Function eXtraction) was the year before or so.

    - PGE (Prioritized Grammar Enumeration) won best paper at GECCO 2013

    I go into the problems and causes in my paper: https://github.com/verdverm/go-pge/blob/master/pge_gecco2013...

    And took a stab at vibe coding with a tiny LLM to port it to JAX: https://github.com/verdverm/pge-jax I'll put some more effort (tokens) into this and get it added to SRBench.

    Fundamentally, most of GP based SR research fights issues with the algorithm (loss of diversity, equation bloat, premature convergence). I have not seen the field really adopt a new mentality in the 13 years since, not that I have been paying close attention, but I do look at the papers in GECCO GP track from time to time.