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Comment by kvonhorn | Hacker News Reader

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.

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They've been quite popular in my field for the past decade and I am doing some work on them right now.

What field are you in?

Particle accelerator physics. They are used in the optimization of charged particle optics.

Purely out of curiosity - is there something about them that makes them better than other optimization algorithms, e.g. simulated annealing, random local search etc, or for continuous functions gradient descent and the like. Seems like GA's are always glossed over as a relic of past times, it's pretty interesting to hear about them in practice somewhere!

We use them because they are particularly well suited for multi-objective optimization. They are also a global techniqe as opposed to gradient based methods and have great scaling properties. Important because each evaluation of the objective function takes ten minutes for us.

GAs have found a niche in a lot of "obscure" engineering situations. Anything where you need to optimize an expensive black box function is at least worth a try with a GA. I know they are used in analog IC design, civil engineer, and aerospace.

They can be very good when there are multiple objectives / tradeoffs. I wrote my thesis on that, which includes an introduction to the concept and how it works if you're interested. http://master.matsemann.com/

Haven't worked on them a lot recently, but AFAIK, the population based nature helps them escape local minima and do a better job at exploring the solution space.

Where are you hearing about them? I haven't personally seen an increase in popularity, but that's not saying much.

Mostly links here on HN, links at /r/genetic_algorithms hitting my feed a little more often, and I'm seeing GAs referenced with designing neural nets in random articles.

I'm guessing that it's the last one that would drive renewed interest if that is indeed what I've been seeing, though I'm wondering if there is interest in applications for GAs outside of AI/ML.

I believe AutoCAD implemented a service which uses a GA to design an optimal solution for desired engineering specs.

At their core, GAs are search algorithms so they are relevant in lots of fields. Combine them with digital simulations (eg. physics, games, war) and you can search at far faster than life speeds.

GA are used for electronic circuit connection design (sorry, i don't the correct english name for the line printed on the circuits that connects different pins). GA are used to solve the problem of laying out them avoiding intersection. GA are used to design antennas. They are often used in travelling salesman like problems. GA and GP would be vey useful also in some other context like finding sequences of actions if only they could benefit from GPU like neural net did, but gpu aren't suitable for GA/GP and it's a pity since in GA/GP you can understand the inner structure easier than in a neural net where simply you don't know why the net took that decision.

I’m sure you could find a way to do GAs on a GPU. If you could have generations measured in billions of individuals and compute all of their fitness, mutation, crossover in parallel it should drastically speed up the algorithm.

That's exactly the point: is it possible? From my understandings, GPU allows massive parallel matricial calculations but that's not the case for mutations and crossovers (which involves random selection of mutate gene). Even so complex fitness functions (that are based on mathematical operation that takes into account more data per point like a moving average) couldn't be converted to matricial calculus.

Circuit route design :)

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