7 ms·
Genetic algorithms works well when 0) there is not enough data for you to train a NN, and 1) you have a really huge solution space for the problem that you ca
by horyzen 7y ago
Genetic algorithms works well when
0) there is not enough data for you to train a NN, and
1) you have a really huge solution space for the problem that you cannot brutal force, and
2) you can encode each solution into a simple ``string'' (chromosome), and
3) the problem you're trying to solve is not very time critical (GA can take seconds to minutes depending on your problem)
Also, GA can actually utilize many CPU or even GPU cores to solve the problem much faster.
- richk449 7y ago> the problem you're trying to solve is not very time critical (GA can take seconds to minutes depending on your problem) Ha. Days or weeks is typical for complex problems.
- horyzen 7y agoIn the specific area I worked on, minutes are borderline tolerable so I didn't think twice before posting. But now that you said it, I totally see how it can go on for days.
- GordonS 7y agoYep, I recently worked on an engineering project, where GAs were used to evolve new designs for large steel structures, with the aim of reducing weight (and, ergo, cost). There were a lot of constraints, and several applications were used at different points (e.g. specialised 3D CAD) - a single generation took around 1 hour, so we had to let it run for days at a time on a cluster to be useful.
- faceplanted 7y agoWhich genetic algorithm were you using?
- GordonS 7y agoI wasn't in the AI team (I was the architect for the cloud infrastructure and backend), but my understanding was it was pretty much a "textbook" implementation (I dabbled with GAs, basic neural networks and swarm optimisation several years ago). I actually kind of surprised, because I didn't realise people still used GAs any more, let alone such a standard implementation.