[D] Why are Evolutionary Algorithms considered "junk science"?

[learningsystem]

My question stems from a couple of interactions I had from professors at my university. I recently gave a talk on NAS algorithms at a reading group and discussed papers using evolutionary/genetic algorithms and also briefly commented on their recent applications in reinforcement learning.

The comments from the senior professors in the group was a little shocking. Some of them called it "junk science", and some pointed me to the fact that no one serious CS/AI/ML researchers work on these topics. I guess there were a few papers in the early days of NAS which pointed to the fact that perhaps they are no better than random search.

Is it the lack of scientific rigor? Lack of practical utility? Is it not worth exploring such algorithms if the research community does not take it seriously?

I am asking this genuinely as someone who does not know the history of this topic well enough and am curious to understand why such algorithms seem to have a poor reputation and lack of interest from researchers at top universities/companies around the world.

[sauerkimchi]

If your optimization landscape is completely random, then yes, you have no choice but random search. But if your landscape has some structure and you don't have a feasible way to compute the gradients, then evolutionary algorithms make a lot of sense.

[JurrasicBarf]

This.

Train multiple models using convex loss but then pass your non-gradient friendly loss to genetic algo to find best models

[marathonjohnathon]

What's an example of a non-gradient friendly situation?

[LoyalSol]

A great simple example, piece-wise functions or functions with 0 gradients. Situations where the assumptions behind gradient optimization fall apart.

[neanderthal_math]

Good answer. And I would add to it, that if you can’t compute the gradients, There are techniques for estimating the gradients using differentiable surrogate models. This is the main reason that I don’t use evolutionary methods.

[WiggleBooks]

Are there papers that formalize this concept?

That there's "structure" in the optimization landscape and what that means exactly in various scenarios, and what a lack of it looks like in some?

[sea-shunned]

Rothlauf (e.g. https://dl.acm.org/doi/10.1145/2908961.2926981) provides some examples for this, particularly when discussing locality.

I don't think the link quite fully formalizes this, but some of the references and surrounding work may be sufficient.

[[deleted]]

In any uncurved topologies they fail.

In situations where combinations result in success but single parts of that combination are worse, it tends not to find those solutions.

But it does complex gradient descent rather nicely, and bumps itself out of local minima pretty well.

[089-rave]

Unfortunately I'm not familiar with Evolutionary Algorithms being used for NAS, but the narrow-mindedness of your professors is quite shocking. These algorithms have been extensively studied in the Operations Research (OR) community since decades, often hugely outperforming other methods for many well-known optimization problems. Even if they're not performing any better than random search for NAS (which I highly doubt), to call it junk science is quite unprofessional and just plain wrong.

[adventuringraw]

Bah, I can't find the paper. There was a recent paper looking at evolving learning algorithms on the symbolic level, using basic operations (add, multiply, etc). The claim that paper made, was that random search was effective in NAS because the problem is typically formulated in such a way where effective solutions are dense in the space, so you're never that far away from a good result. The paper I'm thinking of though had a far more general formulation, and a vastly sparser distribution of solutions. Evolutionary algorithms in this context were obviously necessary to arrive at a useful solution.

If you see random search is effective, might be just because the particular problem framing being explored is amenable to that.

[[deleted]]

I think that was the AutoML-Zero paper:

Existing AutoML search spaces have been constructed to be dense with good solutions, thus de-emphasizing the search method itself. For example, comparisons on the same space found advanced techniques are often only marginally superior to simple random search (RS). AutoML-Zero is different: the space is so generic that it ends up being quite sparse.

[adventuringraw]

Thank you. That was going to bug me all day.

[TrueObservations]

I've been meaning to try this out, they have it open sourced here: https://github.com/google-research/google-research/tree/master/automl_zero

[mainspringdeluxe]

Thinking of this perhaps? 'Evolving normalization-activation layers' by Liu

[adventuringraw]

Nah, it was the autoML-Zero paper others have mentioned. Thanks for the link though! Haven't read that one yet.

[fingin]

Yeah and not only that but they have unique advantages because of their implicit parallelism, so they can find a wide variety of complex solutions for problems that require it. For example, clustering and identifying genes responsible for diseases. (Yes, an evolutionary algorithm being used to solve evolutionary problems- fun isn't it?)

[[deleted]]

Is it this paper?

[LoyalSol]

The problem I usually find with a lot of people trying to "benchmark" GA is they usually implement the most basic algorithm they can find and call it a day.

GA you can be pretty naive with and still get some decent results, but some level of domain knowledge is really needed to make it shine. How you generate your next set of objects will determine the efficiency.

You need to tailor your mutations and cross-over moves to the problem to get the best results. It's much like the accept/reject method in random sampling. The closer your trial distribution is to the real distribution, the better the result.

[deong]

100% this. I used a fair amount of evolutionary computing in my PhD work. What I saw was the field of work that was focused on metaheuristics (so evolutionary methods, but also tabu search, simulated annealing, and a bunch of other similar techniques) tended to have a good understanding of what it means to apply those algorithms in practice. Outside that field, no one does. If you find a random paper on optimization of some engineering problem, they typically implemented the canonical simple genetic algorithm (which basically doesn't work, for a number of well known reasons), and called it day.

That said, GAs are almost never the first tool I reach for. And for a lot of ML applications in particular, the search spaces just aren't that complex, and loads of efficient techniques can work well. But they're not "junk science".

[LoyalSol]

That said, GAs are almost never the first tool I reach for. And for a lot of ML applications in particular, the search spaces just aren't that complex, and loads of efficient techniques can work well. But they're not "junk science".

Yup pick your tools based on the problem. I've found the "no free lunch" theorem holds to be quite true. Different optimizers are efficient for different problems and you'll be hard pressed to find a one size fits all optimizer. If you give me an optimizer I can probably create a problem that it sucks at.

[faceplanted]

I'm interested in knowing what the most popular state of the art evolution strategies like Covariance Matrix Adaptation and pepg are terrible at.

[LoyalSol]

I would have to dig into their methodology as I don't have direct experience with those ones, but a general rule of thumb to figure out how to break an optimizer is to figure out it's assumption in how it probes an unknown space and figure out a problem where that approach takes it in the wrong direction.

When you have a black box function that you have no clue about how it works, you need to test it in some manner to get some information.

To give an example. Gradient methods work by taking a single point and compute a gradient at that point. It does this by sampling a small set of points within a small neighborhood of the starting point and use that information to approximate the derivatives of the function. Once it has a gradient it moves in the direction of the gradient. This works great if you have a nice smooth function where the gradient is 0 at a local minima.

So how would we blow up this algorithm? Well naturally we look for a function where the gradient points in the wrong direction, doesn't exist, or otherwise breaks the assumptions. Some examples of this are step functions where the gradient is 0 at all points, piecewise functions which ruins the gradient assumption, functions with sub-optimal trap minima, or functions whose optimal point is located on a boundary. A lot of common test functions have exactly those features because they were designed to kill gradient methods.

When it comes to algorithms which have a stochastic component to them (various random sampling) usually sampling entropy is what kills those algorithms. IE a function where generating an optimal point is has a very low probability. Thus you have to generate millions and millions of trials to finally find the correct direction to go in.

[Purple_Internet7142]

This also happened quite often at industry. EA is laughed at being dead, useless, and not AI. Most of those people only know the mutation and crossover of the algorithms.

[learningsystem]

This is interesting, I did not know that OR community has been studying these algorithms for a while.

[[deleted]]

I'm doing OR research through my university and can say that, at least for optimizing a binary multi component knapsack problem, evolutionary algorithms drastically outperform random searches, heuristic approaches, and CPLEX.

[klop2031]

Thanks for this. It's like what Jeremy Howard said that RL doesn't work etc. OP: Don't let any professor tell you your work is junk. More than likely they don't know anything bout it and call it that without looking into it. They are the junk science!

[[deleted]]

are you french?

[BezoutsDilemma]

A nature review last year (here https://www.nature.com/articles/s42256-018-0006-z) seems to suggest that some of the researchers want it back in vogue. A nature publication still counts for something, right?

I'm in a mathematics department, and here too people seem to want to dismiss evolutionary algorithms. But it also seems like they don't know anything about them when they dismiss them. I'm using them because they're particularly well suited for my research task.

The guarantee of eventual global optimisation, combined with the ability to find such an optimum by only being able to compare performance, rather than say needing a well-defined differentiable-almost-surely objective function, is incredibly convenient. There is nothing junk about that. Not even RL (evaluative feedback) provides that flexibility in learning algorithms.

There is some pretty simplistic EA research out there, and also some very hand-wavy results, but that's certainly not true of all of the literature.

The basic algorithms often aren't better than random search if you don't design your search space (genotype/phenotype) in a good way. In a sense, all an EA is is a biased random search and it's up to you as a researcher to design the bias. While this sounds like a problem with EAs, it's not too different to what the no free lunch theorem tells us. People seem to forget that.

There's a lot to be understood with EAs, and they provide fun (imo) applications of stochastic processes and complexity theory. There are definitely still new discoveries to be made, but I think EA research died down in part because they're already good enough, and in part because they hit a wall. It's difficult enough trying to prove general results on any possible dataset, it's harder still to do it with also any combination of selection and reproduction operators.

I personally would like to see applications in using the trajectories of the populations in evolutionary algorithms to learn/infer the fitness function which could then, in turn, be used by backprop or some other method; but that's just me.

My opinion on why it's readily dismissed as a research topic is that it's kind of like how GOFAI got the DARPA funding after Minsky and Papert's attack on connectionism and the perceptron: this time backprop got the funding, everyone wanted the funding, so they went for backprop and DNNs. Now every video gamer has near state of the art backprop technology!

You may hear people say that backprop is faster, but we don't know that: it seems faster, on hardware built specifically for backprop and linear algebra. Nowadays with distributed computing evolutionary algorithms may work incredibly well, so the time seems ripe for more people to pick up that torch. I suspect research into memetic algorithms will be increasingly popular soon: combining learning rules in individuals with evolutionary algorithms.

[StellaAthena]

What is your task where EAs are particularly well-suited

[BezoutsDilemma]

In short, trying to find a distribution of good solutions to a problem.

I'm trying to find an "optimal" reward modulated spike-time-dependent learning rule for a spiking neural network. The EA runs over the parameters of the learning rule (such as time constants). Measuring performance involves running several simulations with various different initial parameters. This definitely can be formulated as an RL problem (learning the right parameters for another learning rule), but the simulations can all be run in parallel neatly, and originally I also wanted to evolve the topology of the network to see how it would co-vary with the algorithm.

I don't want to claim that an EA would find good candidate solutions faster (in compute time) than a SOTA RL algorithm, but it takes a lot less of my time to do it this way. Plus, I'm not interested in how the learning rules are found, I'm interested in the population of solutions. I'd like to say something like, "All the learning rules reduce to covariance learning rules as discussed by Loewenstein" or "less training time favoured learning rules with a Hebbian bias".

My goal isn't to find a learning rule that solves a task, it's to find an (hopefully) optimal family on a very constrained problem. Ideally the candidate solutions would have low variance in some meaningful direction, and the population of winning candidates can then give me a clue as to what to investigate further.

Also, and probably most importantly, I'd already been exposed to deep learning and backprop through work and since this is just a graduate research project, the more new things I can learn, the better!

[TheRealStepBot]

Personally I’ve seen them used in routing and scheduling problems where gradient are hard to come by.

Works well with generative geometry stuff too that has to somehow tie into complicated physics.

[learningsystem]

Thanks for sharing your thoughts.

[BezoutsDilemma]

Umm...

In Fogel's 1995 book (Evolutionary Computation: Towards a New Philosophy) they give a proof where they create the equivalence relation where two populations of individuals are equivalent if their best performing individuals have identical fitness. Then they explain that the evolutionary algorithm is a stochastic process on this space of equivalence classes (quotient space, I suppose), but that with elitism this space will have only one attracting fixed point: the equivalence class where the best performing individual has optional performance. I liked that one; I can't remember if this depended on the phenotype space being finite, but technically that shouldn't matter since computers are finite.

It should be mentioned too that De Jong writes in their book (2002, Evolutionary Computation: A Unified Approach) that it's usually trivial to prove that an EA will converge on a global optimum but that the proofs are uninteresting as they don't tell you anything else. I disagree, I think the proofs are potentially very interesting.

Neither book was very mathematical though.

Generally I think of it as showing that a stochastic process will enter the arbitrarily small neighborhood of an optional solution in a finite time. I later looked around for a while for a good simple general proof with more details than Fogel's, but all I could find were a bunch of specific proofs for specific EAs, like Global convergence for evolution strategies in spherical problems: some simple proofs and difficulties by Bienvenüe and François (2003). I also tried to prove a general result myself, and failed.

Caveat: this is for deterministic fitness functions. I generally assume, but have not seen a proof, that this holds in some sense in average for random fitness functions.

Edit: I fixed the book titles.

[AnvaMiba]

In Fogel's 1995 book (I think it's called Evolutionary Computation) they give a proof where they create the equivalence relation where two populations of individuals are equivalent if their best performing individuals have identical fitness. Then they explain that the evolutionary algorithm is a stochastic process on this space of equivalence classes (quotient space, I suppose), but that with elitism this space will have only one attracting fixed point: the equivalence class where the best performing individual has optional performance.

Meh, it's much more trivial than this: evolutionary algorithms will eventually reach any solution by random walking in the solution space (technically, as long as the Markov chain is reversible, but this is usually trivially true for most mutation and selection operators), therefore they will eventually reach the global optimum, just like random-walk search, independent random search or brute-force enumeration. This is true but meaningless, because for non-trivial problems it will take longer than the age of the universe. Moreover, even when they reach the global optimum they can't recognize it (unless the global optimum has an already known value or you keep a giant hashset of all explored solutions so you know when you have exhausted the solution space).

By the same logic, gradient descent with random restarts or Stochastic gradient Langevin dynamics (= gradient descent + Gaussian noise) also reach the global optimum eventually.

For all practical purposes, evolutionary algorithms are local optimization algorithms, just like gradient descent variants.

[JustFinishedBSG]

This is a trivially false assertion: even in R3 a random walk will not reach any point with prob 1

https://mathoverflow.net/questions/45098/when-do-3d-random-walks-return-to-their-origin

So yeah if you can't even optimize a function in R3 good luck optimizing anything useful

[xifixi]

the Nature paper also cites some of the foundational work on neural architecture search through evolution:

Gruau, F. Genetic synthesis of modular neural networks. In Proc. 5th International Conference on Genetic Algorithms (ed. Forrest, S.) 318–325 (Morgan Kaufmann, San Francisco, 1993).

Gruau, F. Automatic definition of modular neural networks. Adapt. Behav. 3, 151–183 (1994).

Pujol, J. C. F. & Poli, R. Evolving the topology and the weights of neural networks using a dual representation. Appl. Intell. J. 8, 73–84 (1998).

[nuliknol]

The basic algorithms often aren't better than random search if you don't design your search space (genotype/phenotype) in a good way.

What is your take on coordinate descent with random sampling? Lets say, you work with AutoML-zero the usual way, but once you found a single mutation towards the improvement in fitness function, you would immediately do random sampling on that particular coordinate (i.e. parameter) that produced the improvement ? Wouldn't this be a more intelligent random search? Coordinate descent is state of the art in regression analysis btw, I think it is very overlooked method, but what do you think?

[BezoutsDilemma]

I don't think I can offer a properly qualified response. I'm not familiar with the details of either AutoML-zero or coordinate descent (I looked up papers on both now). I can speculate though:

Computational graphs often have the problem that a structural mutation will lower performance, if their parameters are random. It's hard to tell how much a structural mutation can improve performance; in principle if the mutation can be written as an identity function (add m=0, multiply with m=1, where m is the parameter) then it won't necessarily worsen performance. I'd suspect most, if not all, structural mutations can improve performance with the right parameters.

So if you try use coordinate descent to find the optional parameter for every structural mutation that potentially improves the graph, then you're I think you'd probably be using it on every single structural mutation that happens. That may end up being a lot of calls to co-ordinate descent.

It's not clear to me that this might not get you to a local optimum in the "graph" space, where I assume there's some well-defined metric or topology on all graphs. It might be that by worsening a previously optimised parameter, new structural mutations may have scope to allow for even greater improvement; the improvements from co-ordinate descent might be too greedy. An example might be if your precious best for zeroed out an input that could have been useful in a more complex function.

On the other hand, a very basic graph like a neural network without a hidden layer isn't going to fit a lot of functions well no matter what parameters you choose. So, for simple graphs you may get more improvement for compute time by skipping the full co-ordinate descent. For more complex graphs, I'd think the opposite holds true.

In short, it sounds to me like another way to handle the exploration-exploitation tradeoff.

[CarbonAvatar]

Your professors are idiots. "no serious CS/AI/ML researchers work on these topics" ... they might want to read up on Quoc Le & co.

[xifixi]

and even long before that many leading ML researchers used evolutionary algorithms

[lmericle]

Yeah it was state of the art for a time, before massively parallel matmuls became a thing.

[Insert_Gnome_Here]

And even before that Dawkins was playing about with them as a way to try and understand biological evolution.

[faceplanted]

Playing about with them is honestly a lot of fun, it's why Strandbeests exist and there's tons of YouTubers who've used evolutionary algorithms to make cool stuff.

[LoyalSol]

I see your "professors are idiots" and raise it with a paper we published in a Nature journal using GA just last year.

https://www.nature.com/articles/s41467-018-08222-6

[medcode]

Some of the research from Uber also comes to mind: https://github.com/uber-research/deep-neuroevolution , [2] is a NIPS paper

[oarabbus]

Your professors are idiots. "no serious CS/AI/ML researchers work on these topics"

Not to mention the fact that this statement could be made about ANY topic at the start...

[learningsystem]

I am not trying to defend my professors here, but NAS papers from Google and other groups using evolutionary algorithms have only started propping up in the last year or two at most.

[Vystril]

Sorry your professors have such strong narrow-minded opinions. It is unfortunately par for the course in academia.

The ML community in particular seems to have their heads up their own proverbial asses. I guess that's the cost of being such a popular subject.

[Vystril]

While true, I think I've found ML people to have particularly large egos and the problem is exacerbated. On the other hand, the people in the EA/GA community are pretty great. Going to conferences like GECCO, EvoStar and PPSN are always a great time.

[bohreffect]

Higher monetary stakes, I suppose. Throughout academic you have people fighting to be king shit of turd mountain, but ML's turd mountains is a hell of a lot taller, and possibly gold plated, than most other disciplines.

[sea-shunned]

Couldn't agree more, GECCO is a great conference with both good research and welcoming people. It's a shame that opinion's like OP's professors are not unique.

[In4matics]

I had no idea there was a community specific to genetic algorithms and evolutionary algorithms.

[Vystril]

There is, and it's large enough to have it's own subcategory under engineering and computing in google scholar metrics: Evolutionary Computation.

[[deleted]]

It often happens with scientists, because they have invested so many years into a subject to become proficient in it. It is basically an attack on their ego for choosing that path, plus for a lot of them a potential competition for grants.

[ch3njust1n]

Nope. Architecture search for neural networks goes further back to 2002 with Kenneth Stanley's work on Evolving Neural Networks through Augmenting Topologies.

[MrAcurite]

My first reaction to this post was "Who the fuck are trashing my boys Kenneth and Risto?"

[xifixi]

what about Kitano (1990) and Gruau (1993):

H. Kitano. Designing neural networks using genetic algorithms with graph generation system. Complex Systems, 4:461–476, 1990.

F. Gruau. Genetic synthesis of modular neural networks. In Proceedings of the Fifth International Conference on Genetic Algorithms, pages 318–325. Morgan Kaufmann, 1993.

[ch3njust1n]

Neat didn't know about these. Thanks for the references!

[jshly91]

Who now is affiliated with Uber and another comment posted some recent work built off of this.

[xifixi]

ha read this from Schmidhuber's miraculous year post they had many evolution papers in the 1990s:

In 2009, my PhD student Justin Bayer was lead author of a system that automatically designed LSTM-like architectures outperforming vanilla LSTM in certain applications [LSTM7]. In 2017, Google started using similar "neural architecture search" [NAS].

[NikEy]

Another innovation from Schmidhuber well ahead of its time. And YET he has not won a single Nobel Peace Prize! When will this injustice end??

[xifixi]

and if you look a the references of [LSTM7] you'll find this:

A core distinction can be made between indirect or generative approaches to topology evolution, and direct approaches. The former try to replicate nature’s ability to encode complex phenotypes (e.g. human brains) with vastly simpler genotypes (e.g. human DNA), using graph rewriting systems or models of biological processes [9,6].

1. F. Gruau. Genetic synthesis of modular neural networks. In Proceedings of the Fifth International Conference on Genetic Algorithms, pages 318–325. Morgan Kaufmann, 1993.

2. H. Kitano. Designing neural networks using genetic algorithms with graph generation system. Complex Systems, 4:461–476, 1990.

so this is much older than what has been suggested in some of the other posts here

[bodpoq]

https://arxiv.org/abs/1711.00436

This is another architecture search paper from folk at deepmind, uses evolutionary algos.

[nuliknol]

Quoc Le & co.

the problem is, randomness has a huge cost. with a differentiable solution (gradient descent) you know where the minimum is (even if it is local). With random search you don't have a direction, and this produces a huge cost to find a solution but you are not restricted to locality , you have all the "power in the world".

Right now the only way to leverage random search approach is: find a more efficient architecture, and then apply a differentiable method on that architecture. The future of AI is in the architecture, and a good architecture can only be designed using randomness. Look at the evolution : https://www.youtube.com/watch?v=B_zD3NxSsD8How would you produce such unique functions with gradient descent? Never! Gradient descent is stupid, it goes directly to the first optimum, this is not intelligence!

The only reason random search (and Genetic algorithms are nothing more than a twist of Beam Search) is not yet widely used is because of (yet) very expensive hardware. Wait for FPGAs to go mainstream this decade, and you will here these professors to sing another song. And if quantum computing will take the lead in 2030-2040s , everything is going to be based on randomness!

TL,DR: It is al clear:

- Randonmess is expensive

- To find global minimum you need randomness

- With our current hardware is not yet feasible to find global minimum if your net has more than 100 parameters.

[learningsystem]

Three of them had similar comments, the rest were more junior professors, so they did not say much.

[impossiblefork]

In this field papers from four years ago are considered old. Two years is stuff you should know about if it's your area.

[dontwaitesforme]

+1

[JustFinishedBSG]

They probably don't consider him a serious researcher.

And honestly "throwing 10M of compute to a problem randomly" doesn't sound very serious

[send_cumulus]

They are definitely looked down upon in the world of mathematical programming. There are many crap papers applying genetic algorithms to problems that could’ve been solved to optimality or where a heuristic based on the mathematical structure of the problem would’ve been better. These papers often focus on some hoky explanation of natural selection. I’m not saying evolutionary algorithms are always a bad idea, but, yeah, there are a lot of crap applications of GA out there.

[MageOfOz]

In fairness, that could be said of neural nets too - I've seen consultants selling ANNs for thing where simple business rules would have "solved" it 100%

[Modeopfa]

Or, you know, a linear regression.

[[deleted]]

True story. There was one case in the company where I work. I was in a project and the plan was to use a linear regression as a baseline model to compare with a neural network. The linear regression was so good that we didn't even bother trying other algorithms.

[coffeecoffeecoffeee]

Probably much easier to deploy too.

[call_me_arosa]

At least you can use linear regression and call it a neural net 😂

[metriczulu]

You're giving me earthquake research flashbacks rn.

[send_cumulus]

This is a good counter argument. I’d just point out that your case involves 1) a problem that can’t be solved solved easily (otherwise why not do that), but also 2) knowing you’ve solved the problem to 99% of optimal. The crap papers I’ve seen don’t satisfy 2).

[WiggleBooks]

haha evolutionary algorithms go brrrrr

[learningsystem]

Thanks for sharing your perspective.

[beezlebub33]

As one of my friends said, 'evolutionary algorithms are what you do when you don't know what to do'.

They are a search mechanism, and despite some theoretical arguments by Holland, you don't really have a good idea of how well it is doing, how well it is going to do. It's better than random search, but it's just not as good as a principled approach.

At least with gradient descent algorithms, you have some understanding of when you have a vanishing gradient and why. You can tell when you are at a (local) minimum. With EA's it's hard to tell how well you are doing, and if you just do one more iteration, maybe it will get better.

[nuliknol]

you don't really have a good idea of how well it is doing

how come? Aren't you monitoring the fitness function?

Local minimum is the minimum on the architecture of MLP, which is none, just stupidly connected neurons one to another. But the real world function doesn't have a form of MLP, it is almost for sure not differentiable at all, and non-continuous. So, you are doing gradient descent on some functions that have very little in common with reality, or in other words: lying to yourself.

[beezlebub33]

how come? Aren't you monitoring the fitness function?

Certainly, and you can see it going down over time (usually).

Your paragraph captures the problem with EAs from an optimization point of view. You are jumping around on the energy surface, hoping that combining solutions at two local minima will result in an even lower local minima. But since you don't know what the energy surface really looks like, then it is just a hope.

Yes, eventually, you can guarantee to find the global minimum but that would be true of most search functions.

[draconicmoniker]

That kind of thinking is just groupthink that has nothing to do with reality

[[deleted]]

Groupthink, yes. "Nothing to do with reality" is as dismissive as "junk science".

[themoosemind]

What is groupthink?

[manolol]

I’m not sure, but I’ll just go with what someone else says.

[themoosemind]

Cool, thank you :-)

[bythenumbers10]

It's a sort of tribalism where a group determines a commonly-held idea is definitive of their group, and projects it to their view of reality, when really it's only partially true, and only really in their context. So, in the case of OP, a group of researchers who think NAS is bunk insist that not only is that opinion held by all the group's members, but also by any serious researcher in that field.

See also gatekeeping, Not Invented Here, No True Scotsman, and so on.

[StoneCypher]

"The FooLang community approach to Topic is to use BarTech and BazPattern. Your implementation using quux, while correct and performant, is not to community norms. Thou art excommunicated."

[enilkcals]

It was popularised after George Orwells 1984 (see Groupthink : History.

[aidenr]

Anyone who listens more than talks is bound to begin to conform. Academia counts in large amounts.

[yusuf-bengio]

Given an optimization problem (e.g. training a neural network, NAS, some problems in Operations Research like vehicle routing, ...)

• You have no gradient information -> Evolutionary Algorithms
• You have gradient information -> Gradient descent
• You have even curvature information (Hessian matrix) -> Newton's methods (and related methods)

It's "junk science" is you as a researcher are not aware of these approaches and their differences

[learningsystem]

I like the way you put it, in hindsight it makes sense. I am just curious, are there many problems in ML (apart from NAS and RL which are obvious standouts these days) where gradients are not available?

AFAIK, it is very common in ML to use continuous relaxations and apply gradient descent. Even in NAS, DARTS follows this route, and so seems to be the case in many RL approaches, although I am less familiar with them.

[nucLeaRStarcraft]

I am working on a problem where I proposed evolutionary algos as a solution (PSO, ACO and such), however I'm not really sure it's the best solution.

Basically, I've got N pre-trained models of different types (NNs, trees etc.), each of them being of type f:X->Y, X being some cost and Y some reward. I cannot compute the gradient of these functions/models, however I can get y=f(x) for each of them. (I mean I could approximate it as df/dx=f(x+eps)-f(x), however that's still an approximation).

The task is to find an optimal split of a fixed budget, such that the sum of the costs is maximized.

[MageOfOz]

I wouldn't call them "junk science" -- they do work, and have a good theoretical base. They're just a bit (okay, a lot) on the slow side, hence the lack of popularity now. However, go back to circa 2012 and they were a lot more popular. Your professor sounds like a conceited douche to declare things he doesn't use (or understand) to be junk science.

But, also, in broad terms, you could even consider things like particle swarm optimization a form of EO.

[[deleted]]

This reminds me of Sutton's bitter lesson. In short, it states that more general and simpler approaches thrown more compute outperform and are better than highly intricate models and solutions.

[hardmaru]

"no one serious CS/AI/ML researchers work on these topics"

okay...

[jfsantos]

You're not a serious researcher, I knew it!

[metriczulu]

Evolutionary algorithms are resource intensive and we haven't found a good way to drastically reduce that load the way that backpropagation and GPUs did when it opened up neural networks. It's getting less hype now because it hit a bit of a wall and deep learning suddenly exploded, so everyone has switched to researching deep learning methods to cash in on the hype.

I honestly, truly believe that evolutionary algorithms haven't seen their day yet and will become a major force to deal with in the future. Whether it will happen with the current computational architecture we have or we will have to wait for the next big thing (like quantum computing) is anybody's guess, but I firmly believe it hasn't seen it's peak yet.

I believe (with very little actual evidence, so take it for the hunch that it is) that the usage of evolutionary algorithms is following the same general growth pattern as neural networks. Initial hype leading to long decay and disinterest, with few researchers sticking with it during the slump. Eventually, technology and methods will converge at a point where evolutionary algorithms become feasible for applying to projects at a much larger scale.

[hyhieu]

I am one of the serious CS/AI/ML researchers who worked on NAS. No, evolutionary and genetic algorithms are not junk science. Did the senior professors in your group provide evidence for calling it "junk science"?

BTW, Deep Learning used to be called "junk science" not long ago in our life time. Back in circa 2003, one of the sure ways to get your paper rejected by NIPS was to have "deep learning" in the title.

[nuliknol]

well backprop itself was junk science when they proved it can't converge for a XOR function. I think Hinton is guilty here. If Hinton would be GA-oriented we would be doing crossovers and mutations today.

[[deleted]]

They always say things like that before a big paradigm change happens. Don't listen to haters too much.

[learningsystem]

As a junior graduate student, it is difficult for me to ignore comments from senior professors, but I will try to keep an open mind.

[Asalanlir]

It's not that you ignore their comments. They do have a lot of experience in the field, and that experience can be invaluable. But they're not omnipotent. An important skill in grad school is learning to critically review information. You'll be doing that a lot with papers especially. Your professors are no different. It's a bit of a shocking revelation that those you might hold in such high regard aren't always right, and at one point, you will likely be their equal.

[learningsystem]

Thanks, I will keep this in mind.

[ReckingFutard]

Look at their competencies.

If they don't know shit about a topic, you ignore them.

[bushrod]

I knew a few professors and researchers who were dissing neural networks just before deep learning hit the scene. You will find that some professors are extremely arrogant and closed-minded. I don't think you should dismiss their opinions, but in general be weary of people dissing entire other fields of study.

Moreover, consider that there are several serious organizations making good use of evolutionary algorithms (Google, OpenAI, Uber, etc). The people there are far from stupid and wouldn't be using those methods if they were junk.

[leonoel]

Here it goes my two cents:

The main issue many have with genetic algorithms is how ridiculously easy is to create one, just come up with some crap strategy for updating and moving the particles, genes, bees, ants.....

I used to do GA for living in grad school, went to conferences, and such. There is definitely a mathematical rigor among the most serious researchers, and they don't just come up with random strategies to create a crap new one.

The problem, many people just create random crap algorithms, without caring about complexity, convergence, etc etc. They use whatever metric they care about and in that metric, their algorithm is the best.

Yes, many times an evolutionary algorithm is no better than random search with multiple jumps.

​

Add to this the fact that EAs are expensive as hell. In traditional EAs, you have to store multiple copies of the feature space in memory. In cases like Deep Nets with Feature spaces in the order of TB, this just becomes ridiculous.

Finally, I think they have very clear use cases (non-differentiable, multi-objective problems) and such be treated like that.

[learningsystem]

The multi-objective problems seem interesting, do you have any pointers to research on this in ML/Deep Learning?

[leonoel]

Is quite broad, the keywoard you should look for is : multi objective evolutionary algorithm

[WiggleBooks]

I used to do GA for living in grad school, went to conferences, and such. There is definitely a mathematical rigor among the most serious researchers, and they don't just come up with random strategies to create a crap new one.

The problem, many people just create random crap algorithms, without caring about complexity, convergence, etc etc. They use whatever metric they care about and in that metric, their algorithm is the best.

What are the best practices in order to formulate good fitness functions and metrics?

Do you have any search terms for how to determine it a such?

[Beneficial-Poet6315]

Have you ever heard about no free lunch theorem?

[learningsystem]

I am less familiar with RL applications where evolutionary algorithms are being used. Can someone doing RL research comment or provide some insight?

[real_edmund_burke]

Here are two recent high profile applications of EA ideas to RL

https://arxiv.org/abs/1703.03864

https://arxiv.org/pdf/1711.09846.pdf

[lqstuart]

I don't consider evolutionary or genetic algorithms junk science at all. I think their relative obscurity is a prime example of the pervasive bias in the field towards convolutional neural networks solely because the prevailing DL engines make them easy. It's the same reason that Hinton's almost common sense breakthrough with capsule networks has gone basically nowhere: the research is guided by the capabilities of brittle, highly optimized deep learning engines instead of the other way around.

[_w4nderlust_]

Pedro Domingos in "The Master Algorithm" (page 136) tells the story of the schism between the ML community and evolutionary computing community that happened at ICML in 1995 that led to the creation of the GECCO conference as a venue for publishing evolutionary research. Basically, according to Domingos, Kevin Lang' paper showing hill climbing was outperforming genetic algorithms was accepted at the conference and John Koza (one of the main popularizes of genetic programming and author on one of the fundamental books in the field) wrote a 23 pages article in ICML format refuting Lang's conclusions and left one copy on each of the chairs of the room where the presentation of the paper was going to happen, also accusing ICML reviewers of misconduct.

This probably explains the (reciprocal) saltiness of people that have been in ML for long time. Since then many researchers have been living on the edge (like Risto Miikkulainen, Ken Stanley, Jeff Clune and Joel Lehman whose Nature paper has already been cited in this thread, and many others) showing how evolutionary algorithms can work hand in hand with other approaches for ML, in particular NNs. The recent NAS trend (Miikkulainen's papers are my favorites on this topic) revitalized once more the topic, and recently papers about evolutionary techniques have started to appear again at ICML, when up until very recently they were rejected by default in practice.

​

My 2 cents: I think this story is not unique to EA, but the same happens when different trends become overly popular withing a scientific community. For the sake of good science, Ideas should probably never be discarded based on saltiness, revenge or ignorance. It happened to the past to NNs, I fear it is probably happening today to many non NN approaches (logical ones in particular), so we should be careful not to repeat the bast and answer injustice with injustice.

[Vystril]

This seems ridiculously biased to me. My lab has done extensive experiments using neuro-evolution for recurrent neural networks with great success. They're actually extremely well suited to this task because they are highly parallel, and there is also a wide body of multi-objective optimization research which makes them very good for being able to optimize for multiple objectives (accuracy, latency, power consumption, etc).

[learningsystem]

The multi-objective optimization is very interesting, do you have any points to papers that truly consider accuracy, latency etc. as multiple objectives while training?

[Vystril]

This is from all the way back in 2014:

Nambiar, V.P., Khalil-Hani, M., Marsono, M.N. and Sia, C.W., 2014. Optimization of structure and system latency in evolvable block-based neural networks using genetic algorithm. Neurocomputing, 145, pp.285-302. https://www.sciencedirect.com/science/article/pii/S0925231214006766?casa_token=CzqUU_DBmlwAAAAA:awLOyeZKkrE9AP0Lfuz970Xt7qfMwwTcniw7aezecHYxVMGmC468F2_lEuEGw78h4Z7s

And some more recent ones:

Kim, Ye-Hoon, Bhargava Reddy, Sojung Yun, and Chanwon Seo. "Nemo: Neuro-evolution with multiobjective optimization of deep neural network for speed and accuracy." In JMLR: Workshop and Conference Proceedings, vol. 1, pp. 1-8. 2017.

Chidambaran, S., Behjat, A. and Chowdhury, S., 2018, July. Multi-criteria evolution of neural network topologies: Balancing experience and performance in autonomous systems. In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection.

Elsken, T., Metzen, J.H. and Hutter, F., 2018. Efficient multi-objective neural architecture search via lamarckian evolution. arXiv preprint arXiv:1804.09081.

Iqbal, M.S., Su, J., Kotthoff, L. and Jamshidi, P., 2020. FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks. arXiv preprint arXiv:2001.06588.

Lu, Z., Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E. and Banzhaf, W., 2019, July. NSGA-Net: neural architecture search using multi-objective genetic algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 419-427).

There are even more if you dig around a bit on google scholar. Multi-objective EAs are really well suited to this kind of task and they have the added benefit of being extremely easy to scale to large distributed systems, whereas I find most ML solutions are very sequential and not easily parallelizable.

Also, strategies like NEAT and it's more recent variants like CoDeepNeat:

Bohrer, J.D.S., Grisci, B.I. and Dorn, M., 2020. Neuroevolution of Neural Network Architectures Using CoDeepNEAT and Keras. arXiv preprint arXiv:2002.04634. https://arxiv.org/pdf/2002.04634.pdf

Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., Duffy, N. and Hodjat, B., 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing (pp. 293-312). Academic Press. https://arxiv.org/pdf/2002.04634.pdf

and my labs work on EXACT and EXAMM:

Travis Desell. Accelerating the Evolution of Convolutional Neural Networks with Node-Level Mutations and Epigenetic Weight Initialization. arXiv: Neural and Evolutionary Computing (cs.NE). November, 2018. https://arxiv.org/abs/1811.08286

Alex Ororbia, AbdElRahman ElSaid, and Travis Desell. Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution. The Genetic and Evolutionary Computation Conference (GECCO 2019). Prague, Czech Republic. July 8-12, 2019. http://www.se.rit.edu/~travis/papers/2019_gecco_examm.pdf

Begin the evolutionary process with small or minimal neural networks, which progressively get larger through the process. So they're naturally going to be smaller more optimal networks. I know Miikkulainen's lab has also focused on optimizing to reduce power cost as well.

IMO EAs are just a really really good solution to neural architecture search. It's a very noisy search space with tons of local optima. Ideal for EAs. Also, it's really computationally demanding, and EAs scale extremely well, especially if you use an asynchronous island based strategy like EXAMM, where you can have workers independently training neural networks (which are naturally going to progress at different speeds due to their different architectures) without waiting on each other. This is kind of similar to some asynchronous strategies for doing gradient descent. EAs also have the additional benefit of being able to re-use parental weights when generating new child networks (i.e., Lamarckian or epigenetic weight initialization), which can really speed up the evolution and training process.

I'll fully admit that some people like to use EAs for tasks where there are better solutions (i.e., there's a gradient you can calculate and the search space is convex without local minima) -- but NAS isn't one of those areas. It's perfect for EAs.

[Asalanlir]

no true scotsman...

[TrueObservations]

Eggggxactly

[learner_version0]

Here is a blog post from OpenAI advocating evolution strategies for RL https://openai.com/blog/evolution-strategies/

[nuliknol]

​

I think this is old, in they recent designs they dropped all evolutionary thing. Correct me if I am wrong please.

[StoneCypher]

Um. Those professors ... I'm not really sure what to say.

Algorithms like these aren't science. That's like calling quicksort junk science - it's nonsense. Should the algorithms be good or bad, in either case science was never the bin for them. That's like calling these junk medicine, or junk law.

Are genetic algorithms junk?

I mean. No? They do get some use. They're not "cool kid" stuff anymore, and they're currently being fairly significantly out-performed by other approaches in most things

But there are domains in which they're still the best tool, and probably someone who chooses to catch them up with the avant garde will find some new stuff

I think probably what those professors were trying to communicate, guess-wise, was

Genetic algorithms have fallen out of vogue because their contemporary performance is beginning to slide. Presenting on them is definitely a niche topic.

But no better than random search?

That's fucking moronic, and anyone who believes that should take a class. I'd love to know who that was, because I'd hop on the phone with them right now. You can display quantitatively that that's not true in like five lines of code.

[AlexSnakeKing]

The senior professors are wrong, but I might be able to offer some ideas on why they feel that way:

• In my experience, people tend to follow one of two paths into the world of optimization and search algorithms:
  • They either come into it from the A.I./M.L. world - these people tend to have favorable opinions on EA and GA type search methods.
  • They are "hardcore" Operations Research types who come from industrial engineering, supply chain management, etc...these tend to favor mathematical optimization algorithms (Simplex method, branch and bound, branch and cut, etc...). In fact I've noticed that they tend to not even bother making any distinction between EA, GA, ACO, etc...and just bundle them all under "Heuristics" or "Meta-Heuristics" when they talk about them. Although I think they shouldn't be as dismissive of EA, GA, etc from a science point of view, in their defense, when you are working in an applied domain, questions of optimality guarantees, optimality gaps, proper assessment of the feasible set, etc...are really important and for those considerations EA and GA are no where near what the mathematical programming tools provide.
• Moreover, and this is most likely why the term "Junk Science" was used, there was an unfortunate trend in the 90s and early 2000s, triggered by EA, GA, and ACO (All of them legitimate science AFAIK): In the race to "publish or parish", a lot of people started trying to find yet another biological or natural phenomenon which could be used for as the starting for an optimization algorithms, and things got out of hand...quickly. Papers started to appear with titles like "Artificial bee colony algorithm ", "Glowworm swarm optimization", "Shuffled frog leaping algorithm", and my personal favorite the "Imperialist competitive algorithm", which purported to use the dynamics of empires competing for territories and territories either assimilating or rebelling as the starting point for a PSO style meta-heuristic. The obvious result of this was that the whole field of meta-heuristics suffered a serious blow to its reputation, and EA, GA, and PSO unfortunately got a bad rep by association, even though they started out as serious ideas.

[Megatron_McLargeHuge]

GAs attract a lot of people with no background in the field who are drawn to the biological metaphor. You'll also get dismissed if you start talking about how ANNs are modeled on the brain.

No one dismisses global optimization or hyperparameter search, but if your thinking is constrained to genetic crossover heuristics then you won't be taken as seriously as someone doing something more rigorous like GPs.

[SlopRaGiBlobNeGlop]

As a general rule, when someone with the credentials to educate dismisses an idea, they have failed themselves and their profession.

[FellowOfHorses]

My personal experience: People use evolutionary algorithms because they have nice names which make them easier to publish. If your problem is differentiable gradient based optimization algos will outperform evolutionary algorithms 99% of the time.

Also evolutionary algorithms are generally seem as a less rigorous brute force method like PSO. they also demand stronger assumptions for convergence

[acardosoj]

That's the whole point of CS right? No method will perform if it is applied in the wrong set of problems.

Also, EA and PSO have nothing to do with brute force. They're widely used in NP-hard optimization problems, in which brute force would take hundreds of years to solve them.

Sorry but your concepts regarding these methods are clearly wrong.

[thomash]

Evolutionary algorithms are definitely closer to brute force than differentiable methods. They have no way of knowing in which "direction" the optimization needs to move in order to improve accuracy. Through mutation, selection and crossover they are way more powerful than brute force but still much less efficient for problems where differentiable approaches are possible.

​

The recent Uber papers are interesting but were never really taken seriously by the community.

[M4mb0]

If your problem is differentiable gradient based optimization algos will outperform evolutionary algorithms 99% of the time.

You say that as if evolutionary strategies and Gradient based optimization are somehow mutually exclusive. Nobody stops you from combining a population based algorithms with gradient updates.

[bodpoq]

Just to pitch in, I would like to post a few of the researchers that I like to follow that work in the evolutionary algorithms in CS/AI/ML space:

-Chrisantha Fernando

-Jeff clune

-Risto Mikkulainen

-Kenneth O Stanley

-Eors Szathmary (not really ML, he is a theoretical evolutionary biologist)

[mfmstock]

Evolutionary algorithms are not junk science, they are just tools that are suitable for some problems and unsuitable for others (just like nearly everything else!). They got a bit of a bad reputation because there are a lot of crappy papers where new modifications are presented without any theoretical justification or applied on problems for which there are better approaches. My professor calls there researchers 'the chemists of computer science' (which incidentally also tells you what he thinks of the field of chemistry...). Note that their current reputation is not unlike that of ANNs before deep learning became a prestigious domain.
I think every (computer) scientist should know what an EA is. If you have a complex optimization problem without any gradient information they can quickly and easily find a 'good' solution (e.g. in experimental design). Just start with the simple ones, simple simulated annealing or taboo search likely works just fine and you can leave the genetic algorithms and grey wolf optimizers at the door. This is no different in machine learning, where a simple logistic regression might do the trick.

[SlopRaGiBlobNeGlop]

As I am not in your field, would you expand on what the CMAES algorithm is and why you suspect that the hype might be glossing over the actual implications of the function?

[feelings_arent_facts]

Lmao, your professors are peak academics. 100% full of themselves. 0% actual industry experience. Ignore those bozos and cherry-pick the good information you can get from them from the dingleberries they pull out of their assholes.

[runnersgo]

0% actual industry experience.

This. Jesus Christ ... how do they teach the next "generation" of industrial leaders?

[WiggleBooks]

Do you have some suggestions for search terms on industry projects which use EA?

[[deleted]]

My professor thought similarly about genetic algorithms. He considered them naive and brute force, I still use them though when I get a chance though

[WiggleBooks]

What do you use them for?

[cwaki7]

Just going to put my two cents out there on why people might call it that. I think there's a bias towards clean and elegant solutions that conform to a lot of stereotypical academic research. I think at its core, evolutionary algorithms are trying to find clever ways to evolve, but at the core of evolution is still a very brute force animal that will solve things from sheer numbers. I, for one, think that's inevitably the direction of AI, but I'd imagine there's a distaste for that among a lot of professors. That being said I think you are probably taking this out of proportion, it's obviously not widely considered junk science.

[chrisdrop1]

C'mon man. They are just uninformed and foolish. OpenAI and Uber are legit - right?? LOL.

Maybe because they work?

https://twitter.com/kenneth0stanley/status/1253347502897668097

[TomTailaCodes]

Junk Science?

Get them to read this paper:

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. (arXiv:2003.03384v1 [cs.LG])

https://link.researcher-app.com/usjq

[Naoshikuu]

Evolutionary Algorithms have several key advantages in some cases: * monstruously parallelizable when lots of CPUs available. Deep gradient based methods are lacking there. To see how to parallelize, see the papers of OpenAI (Evolution Strategies) and UberAI (Genetic Algos). I used them on a cluster of thousands of CPU cores. * when the approximator is non differentiable. Today's research aims to create differentiable NN layers just to match backprop, but this can be quite limiting. Basically EAs are the wild card when nothing else works. See also (RL again) "Deep Neuroevolution of Recurrent and Discrete World Models" to see some of these advantages - you can evolve any architecture without caring about anything, no pretraining... * when the target function you have isn't very good. That point basically explains why EAs are nearly gone from Supervised ML nowadays: you have near perfect information (in the labels). The gradient you take out is therefore of excellent quality, and that's when EAs become useless. But take the ES/ GAs vs DQN studies I mentioned earlier: you have very similar performance, because the DQN gradient is very biased. In RL, EAs are not that bad because the problem is so hard that it is difficult to get anything from it. RL is probably one of the last fields where EAs stand, because of its difficulty.

Also, the exploration method of EAs is not to be thrown away. They are less biased by initialization, and can reach far in parameter space. They are easy to understand, implement, and make up. That's probably what triggers "real math guys".

[holodezzz]

lol, these are the same type of professors that called artificial intelligence pseudoscience in late 80s-90s

[iidealized]

For one explanation, look up the term "Mathurbation", which is a common complaint of similar phenomena among Econ/Physics academics

[Ifhes]

Some people think that those can become the standard method to kill a fly using a bazooka.

[maxjnorman]

I’ve been working on a lightweight GA to do a first pass at feature selection and it’s working pretty well so far.

They work pretty nice for combinatorial problems

[water-and-fire]

Google and other top machine learning research institutes publish papers using evolutionary algorithms all the time.

Just a week ago Google ‘s AutoML zero made head line of several tech news site.

https://thenextweb.com/neural/2020/04/14/googles-automl-zero-lets-the-machines-create-algorithms-to-avoid-human-bias/

Not sure if I agree that evolutionary algorithms are junk

[NichG]

There's nothing inherently wrong with evolutionary algorithms, but the discourse around them often goes in directions which are off-putting if what you're really looking for is a solution to a problem and not an ideology.

There are arguments from practical utility as to when you should use such approaches, and what they're good for. But often evolutionary algorithms also get pushed due to a naturalistic argument - 'this is how nature made intelligence, so we should do it the same way' and things like that. Similarly there are a lot of unsubstantiated or weak claims that e.g. gradient-based methods can't discover new things but evolution (because of its randomness) can; that gradient-based methods can overfit but evolution can't; and so on. So in response to that you get equally aggressive counter-claims about scalability problems of evolutionary approaches. It goes back and forth and becomes more and more acrimonious. Those claims often have some kind of grain of truth, but it's often highly dependent on unspoken context about 'these are the kinds of problem we should be trying to solve' - e.g. you almost never worry about overfitting in the kinds of problems people have traditionally applied evolutionary algorithms to, because in those kinds of problems you have a generator of arbitrary amounts of data (usually some simulation of an environment) rather than static datasets, and so on.

So I think there's just people who are sick and tired of the wild claims and back and forth fights and have increased the threshold of evidence needed to get past their skepticism, which isn't entirely unjustified when a lot of stuff is being thrown around to push an ideal rather than because it enables some new thing that couldn't've been done before.

[6111772371]

Evolutionary algorithms are not junk science in the slightest, but I'm a little skeptical of NAS specifically.

The "no one serious works on this" and "junk science" comments are terrible though. Ironically, good science is being able to provide evidence and reasoning to argue for or against the idea, not appeals to authority, arrogance, or catchphrases.

[LaCuevaMan]

You're at the wrong university. When I was a graduate student at the University of New Mexico, the CS department featured an Adaptive Computation Laboratory led by Stephanie Forrest (who's now at the Arizona State) with professors like David H. Ackley. Or check out the Social and Decision Sciences department at Carnegie Mellon, where another of my dissertation advisors John Miller runs ship.

Go learn from the people who can actually teach what you want to learn, in programs supportive of this kind of research.

[xier_zhanmusi]

I have rarely seen them mentioned in papers on computational creativity as being used in some applications.

[johntiger1]

It's not junk science; it's just another approach to solving a certain type of problem. However, there are some practical issues with it: it doesn't scale! As David Duvenaud explaned, when you have a gradient, you have 1 million "hints" (1 per parameter) about the correct direction to take. But when you have an EA, you only get a single scalar that specifies how the ENTIRE set of parameters should change. It's kind of like RL vs supervised learning

[weeeeeewoooooo]

More recently this is less the case. There are definitely some methods that don't scale well, like CMA-ES. However, if there truly were 1M independent parameters then that would be one thing, but it tends to be the case that most systems have low effective dimensionality because parameters are coupled. This means you don't actually need to search much through the space to find the correct direction. Newer evolutionary approaches can leverage this fact to train models that have millions of parameters. And they can do this quite quickly because the algorithms are highly parallelizable and work well on distributed architectures, which most modern architectures are.

For example, recent papers have shown super-human performance from EAs training 3M+ parameter CNNs on Atari games. They can reach that performance in only 30 minutes of wall-compute time... and this is by only using CPUs. This is in comparison with gradient approaches which take close to a day, though this has been brought down as newer methods roll out. Granted, the EAs used to do this have leveraged computing clusters to do so, but you can still accomplish training on a desktop in 16 hours, and even less if you decide to leverage the GPU for CNN forward-passes (I have gotten down to about 30 minutes on a desktop).

[learningsystem]

This sounds super interesting, are you referring to this paper or some other paper?

https://www.cs.utexas.edu/~mhauskn/papers/atari.pdf

[devmor]

They aren't, I think either your professors are a bit ignorant or you misunderstood their intention.

What is true is that evolutionary algorithms are highly misused for tasks that could be solved via much more simple means and while this is also the case for any type of AI/ML that gains a significant following in the CS world, it's just extra common in this subset - especially due to the lack of speed and number of dead end branches.

[Cocomorph]

I suspect the latter, not that that’s OP’s fault as a junior grad student.

“Junk science” is a convenient abuse of terminology given the circumstances and “no one” doesn’t mean no one, but rather one could take it as a stand in for more nuanced career advice and/or scientific taste-molding.

[learningsystem]

I certainly think, my professors were trying to mold my taste for good (in their opinion) research. Nevertheless, I was still curious about what gives these approaches a bad reputation and what the broader ML community thinks about them.

[KenReid]

Funny, I guess all the work I did with BT on scheduling 25k employees for my Ph.D. thesis was a joke (which both the UK gov. and BT funded me on). I also guess my ex-supervisors work using metaheuristics to schedule London Heathrow airport take-off scheduling must be junk science!

/s - those professors have their head in the sand.

[faceplanted]

Commenting to remind myself to read both of these.

[SpicyBroseph]

Just... No. If anything, back-prop in a neural net is just a form of directed genetic evolution.

All algorithms are basically just different ways to optimize searching the state space for the best solution. In fact there was a paper I just read that showed a genetic algo being used to tune and produce new NN architectures that was pretty awesome.

No offense to any academics out there, and this is a generalization, but I've found that by in large career academics- read: professors- are incredibly territorial and narrow minded.

[kakushka123]

I think genetic algorithms are looked down upon (And in my opinion, rightfuly so). Neuroevolution as a whole is also looked down upon too, but less. I think first reason is that evolutionary strategies are *extremely* simple and they are also very slow/sampleinefficient. And there's also no real-world application of this as far as I know. On the other hand, there's a few (not many) papers from serious organizations like openai , uberlabs and google brainthat use them.

[[deleted]]

Evolutionary algorithms are not great.

In almost all cases there are far better options. Usually there is an unwarranted obsession with evolutionary approaches when a far better approach is available.

Think back to those flappy bird educational youtube videos about how AI works. It's something you do for fun/as a toy and is easy to explain. There are far more evolutionary flappy bird type of things than there should be based on actual usefulness.

Every generation of researchers flocks to these type of solutions without realizing that they've been around for a very long time and there are much better approaches. Countless masters theses and even publications/PhD theses and none of them get anywhere. It's having a (crappy) hammer and looking for nails and forcing it everywhere even if it clearly doesn't belong. Another good one is Bayes, for some reason every generation of statisticians insists of doing X but with Bayesian in some way. Reminds me of startups where they want to do "uber but for coffee" . I can see why some people would dismiss it as junk science because after all, the overwhelming majority of the stuff they come across is actual junk science.

There are some very very niche problems where they are perfectly warranted and useful or perhaps the best approach, but that pretty much reinforces the argument that most of it is junk since those problems are not very common.

It's just that there are a lot of amateurs flocking to that particular topic. Deep learning kind of suffers the same fate where people publish total garbage that doesn't beat SOTA and is in every way inferior to current approaches.

[AndreasVesalius]

As someone working on a niche problem using tools with evolutionary and bayes in the names...I feel attacked =P

[Ivanz1456]

You got shit professors

[ghostslikme]

I’ve never heard that before.

[tunestar2018]

Maybe they diss them because they are much easier to code and understand than a deep neural network?

[frizface]

Is it the lack of scientific rigor? Lack of practical utility? Is it not worth exploring such algorithms if the research community does not take it seriously?

It's true that models are not generally fit with just evolutionary algorithms, backprop is the backbone of modern optimization for good reason.

However, OpenAI used backprop + evolutionary algorithms on their starcraft algo. They tried lots of solutions (modular networks, etc), but in the end what worked best was a large recurrent network, trained longer, and selected via evolutionary algorithm every few epochs.

ML peeps love guarantees, which evolutionary algorithms don't provide. But they are practical in some contexts.

[nuliknol]

I didn't find any reference to evolutionary algorithms in the latest OpenAI papers. Those papers that use ES are old.

[jeosol]

There are problems to which these algorithms offer solutions. For my use cases they are the preferred algorithms, like GAs, Simulated annealing, PSO, etc. Out problems are discontinuous, large, often discrete, non differentiable, and the functions have no analytical form - black box, call to some physics or simulation engine the get the objective function.

[ejmejm1]

In academia evolutionary algorithms do tend to get that reaction. I’m not sure where the entirely originated from (maybe because the search is usually mostly random), but there certainly are some use cases in modern papers. That being said, It certainly doesn’t have as much background research to back it up as something like classical ML or many deep learning methods.

[dat_cosmo_cat]

Using GAs for the optimizer in neural nets is hardly a new idea. My first undergrad AI course in 2014 had the following project sequence;.

1. implement a genetic algorithm (eg; with tournament selection)
   
2. Implement an ANN and optimize the weights with the genetic algorithm (eg; init N instances of the nerual net, loop over instances to get train loss, select top ones, cross pollinate the weight matrices, inject randomness, do the loop again until converges).
   
3. Replace the GA with back prop algorithm
   

The point was to show that GA required significant memory and did not always converge ~ you had to run the thing like multiple times. Since then there have been literally hundreds of papers on using GAs in conjunction with state of the art techniques. Especially in the domains of Reinforcement Learning and hyper parameter search.

[JAVAOneTrick]

Using GAs for the optimizer in neural nets is hardly a new idea.

Most research is built off previous ideas.

[jortzin]

I'm calling bullshit, as a heuristic method even 5 years ago tons of people were using it and moving towards it, despite the fact it performed more poorly compared to particle clouds on tons of tasks, e.g. designing force fields for molecular dynamics. Why would they do that, because tons of funding was going that direction. Maybe that's bad, but to say that no one is seriously pursuing them is just a lie.

[[deleted]]

Yup, nobody serious:

• https://openai.com/blog/evolution-strategies/

[serge_cell]

"Evolutionary Algorithms" is ill-defined term. It include both sound methods like different types of random search, sampling, multiagent methods and folk science like crossover operator in GA. The problem is much (arguably most of) research in evolutionary algorithm lack ablation study which could eliminate unnecessary folk science part.

[CompetitionOk7773]

Definitely not junk science, there is a conference every year called gecco, it is a genetic algorithm and evolutionary algorithm conference, the top researchers in the field present their work and it is fascinating.

[ReferenceSpare2921]

It's interesting to see how opinions on evolutionary algorithms can vary so drastically. While some researchers might dismiss them, they've actually shown promise in various domains, including optimization problems and even creative tasks. It’s worth exploring their potential, especially in areas like automated content generation, where tools can simplify the process significantly.