3 ms·
Broadly speaking, the evolutionary computation literature is in a dire state. Terrible methods sections, nonsequitur results, endlessly tortured biological anal
by sevensor 2y ago
Broadly speaking, the evolutionary computation literature is in a dire state. Terrible methods sections, nonsequitur results, endlessly tortured biological analogies. The real successes in the field are old at this point and a lay person would have a hard time finding even a competent review paper to point them in the right direction.
- verdverm 2y ago100% I talked about this in my paper. Incredibly, a paper by the top GP researchers had come out the year before (iirc) talking about these very problems. They offered a set of standard benchmarks they hoped the field would adopt, so naturally I did, but I haven't paid attention since I graduated so I don't know if it actually took hold.
- sevensor 2y agoBenchmarks. That’s one of the big problems in the field; since it’s so hard to get people to agree on what we’re even trying to accomplish with an evolutionary search heuristic, it’s difficult to measure performance objectively.
- verdverm 2y agoYup, methods and reproducibility as you have also mentioned. My work focused on Symbolic Regression, the problem, which I tried to separate from Genetic Programming, the algorithm. The literature refers to SR as the algorithm, but I think that is incorrect. I wrote a deterministic algorithm Prioritized Grammar Enumeration and put my code & data on github. https://github.com/verdverm/pypge https://github.com/verdverm/pypge (pdf there too, don't tell ACM)