> evolution of evolvability and then a seemingly unrelated subject: the evolution of robustness.

Isn't there a an analogue to this in software engineering?

There is an extremely large space of programs that produce the exactly same output on a given input, but we humans favor programs that we can read and understand. We also favor programs that are modular and composable, because bits of those programs tend to get reused to create new programs and are easier to write than new 'blob of code' programs.

One can view programs as memetic 'self-replicators', with a selection process dominated by the preferences of human readers and writers.

If we analyzed many dependency graphs between code modules, or a graph of function call usage between snippets, I bet you'd find that actual human-written programs have the same 'sparse' connectivity that you see, because maintainability implies writing code along sane API abstraction boundaries.

TL;DR evolutionary pressure might means nature naturally abstractions within genetic code

The mechanism for evolution is often the opposite of abstraction. Living things tend to make a copy of some really important bit of 'code' and then mutate that 'copy' rather than say losing the ability to form cell walls.

On the other if a cell is making X then the new process can easily leverage the existence of that X when doing something new.

Net result copy paste coding + dependency hell.

I have always seen evolutionary algorithm as a version of gradient descent that throws away the gradient, and instead just goes randomly in all directions until it finds something that works (which is extremely wasteful).

I suppose this is wrong since people still find these techniques useful, but what are the advantages of these techniques compared to gradient descent? (other than the fact that you don't need your fitness function to be differentiable)

> I have always seen evolutionary algorithm as a version of gradient descent... which is extremely wasteful

Evolutionary algorithms work by sampling a combinatorial solution space, and learning about the interactions between solution components. This is made explicit (and much more elegant) in the definition of EDAs:

https://en.wikipedia.org/wiki/Estimation_of_distribution_alg...

By contrast, gradient descent makes the assumption that such interaction effects don't exist or don't matter, which doesn't work for most optimisation problems - you get stuck in local optima.

Thanks! That link was really helpful. I guess some optimisation methods using a Hessian take those interaction effects into consideration, but they are too expensive and not used in practice.

> doesn't work for most optimisation problems - you get stuck in local optima.

This bit is not necessarily true. In practice, and given the high dimensionality, it is (apparently) quite hard to get stuck in local optima. [0]

[0] https://www.quora.com/How-come-neural-networks-dont-get-stuc...

> This bit is not necessarily true. In practice, and given the high dimensionality, it is (apparently) quite hard to get stuck in local optima. [0]

In almost every optimisation problem I've encountered in practice, you do get stuck in local optima using simple techniques like SGD. Interaction effects are everywhere, and create local optima.

Evolutionary algorithms may also let you evolve the architecture. Think of a fly brain evolving into a human brain using the same genetic toolbox. With learning algorithms your assuming a fixed architecture. Hard to think how we’d get open ended evolutionary AI without evolutionary algorithms. At that point learning algorithms is the context dependent optimization step after neurogenesis.

The terms you are using are too vague, and seem disconnected from the reality of these algorithms.

How do evolutionary algorithms let you evolve the architecture? You define a model space, and explore it stochastically (with different evolutionary strategies). The model space you are exploring is linked to a specific architecture.

Regardless, there's also efforts in Deep Learning to automatically learn optimal network architectures ("AutoML").

Of course you can mix your gradient descent with simulated annealing. But that gets close to evolutionary algorithms.

The fact that you don't need your fitness function to be differentiable is a big advantage :) if you had the gradient it would always help to use it.

In reinforcement learning: evolutionary algorithms work by applying peturbations in the parameters space, not in the action space. If your problem is sensitive to action-space perturbations (requires consistent strategies) it might not be possible or efficient to use "standard" RL. Evolutionary strategies (ES) are quite competitive in RL, especially paired with some dimensonality reduction technique (unsupervised VAE).

Local minima are not really an advantage. ES are nearly local - they sample the space around the current solution to approximate the gradients. They'll get stuck in local minima sometimes too (if there are any). Bayesian optimization is more interesting, as it's actually global.

"what are the advantages of these techniques compared to gradient descent?"

Well, the really big advantage is that simple gradient descent can get you stuck in local maxima (or minima, depending on how you look at things). Many fitness landscapes are guaranteed to produce suboptimal results if you use gradient descent. By contrast, a sufficiently large random variation will eventually "get out of the local hole"/"get past the local hump".

It doesn't necessarily have anything to do with differentiability; consider the graph of 0.5x+sin(x). A gradient descent technique is going to get stuck on that one right away, while random exploration won't.

Edit to address some stuff that dragontamer brought up below:

A good genetic algorithm is mostly going to produce offspring that are close to the parents (recombination, which de facto is similar to hillclimbing/gradient descent/gradient ascent) but occasionally it's going to try something off that's completely off the chain (mutation). Some of the early work with genetic algorithms focused on mutation, but it soon turned out that recombination was better most of the time. Nonetheless, you still need some mutation, or you run the risk of getting stuck at local extrema.

I think the premise (neural nets get stuck in local optima) is not trivial, and there has been a lot of research about non-convex optimisation showing that this is not much of an issue. I am not a researcher, but this answer [0] points to this research.

I would also say that there are multiple ways to escape local optima (setting a larger learning rate, multiple random initialisations, ensembling).

https://www.quora.com/How-come-neural-networks-dont-get-stuc...

You say "not much of an issue" but your link says that it's an "open question that is probably being worked on in the community".

Those aren't the same thing at all.

Minima/maxima in high-dimensional spaces are not much of an issue; the bigger issue are saddle points.

cf https://arxiv.org/pdf/1406.2572.pdf

This is presuming that the problem has a continuous formulation anyways.

Saddle points are not much of an issue too: https://arxiv.org/abs/1710.07406

To add to what others have said, local extrema are actually quite rare for "random" high-dimensional functions. This is suggested by a simple heuristic based on the Hessian matrix. As the dimension increases, it becomes exponentially harder for every eigenvalue to have the same sign (coin flipping experiment). So it stands to reason that saddle points should be far more common, and SGD is actually quite good at overcoming saddle points.

This does not accord with my reading of history. GAs (heavily crossover-based evolution) were failing pretty hard at optimization until the early '90s, when they adopted some good ideas (real coding, better mutation operators) from the (largely mutation-oriented) ES community. If you look at the most effective single-objective evolutionary algorithms today, they're mostly descendents of ES, like DE and CMAES. Honorable mention to the micro-GA as a descendent of the GA lineage, but its effectiveness is largely driven by discontinuous mutation events. The Goldberg-style GA is woefully out of date and not particularly useful.

One thing I don't see mentioned here is flexibility. You can plug any ugly old thing into an evolutionary algorithm and let it run. Give me a working computational model at breakfast time and I'll have runs going by lunch. Cranky ancient FORTRAN that requires you to write a new input file for every evaluation? No problem. You have to compile the inputs into the model to make it run? Fine. Badly scaled inputs or outputs? EA doesn't care. More than one objective? Great! Population-based search is a natural fit for multiple objective optimization.

As long as you're clear on what the decisions and objectives are, and you're able to run the model yourself, layering evolutionary optimization on top is easy.

I think the multiobjective-part is very important. In other algorithms, you often have to specify the solution-space before hand. For instance, when optimizing between lightweight and strength, one would have to beforehand say how to weight those two properties. (f = 100s - 5w for instance). This throws away a whole dimension in your search space. For EAs, you can let it roam free, and select the bests tradeoffs from the pareto-set after the algorithm is done.

Yes, exactly! And it gets even better. With a good MOEA, you can keep track of your evaluations, do a short run, decide your objectives don't really express your understanding of the problem, and seed your old evaluations into a run with new objectives. This means you can change objectives without repeating computationally expensive model runs.

The IMO, most interesting work in EA is in 'search space illumination', MAPElites, and it's variations are a great example.

Thanks for pointing this out. I had a look at http://www.evolvingai.org/files/1504.04909v1.pdf. It's an interesting read, although I'm not sold on the value of feature space exploration. I think it muddies the waters around the problem of balancing optimization with diversity, by lumping decisions and constraints together. What's to stop it from over-sampling regions of the decision space that have a steep gradient in the other features?

What's to stop the discovered solutions from being similar? Nothing but the dimensions of interest. If the best solutions with respect to features a, b, and (a&b) are all the same, then it does not seek different solutions.

This may indicate you chose reduntant features though.

Here's an interesting application that provides some motivation. https://arxiv.org/abs/1407.3501

Regarding the similarity of solutions:

If, in a particular region, your model outputs are particularly sensitive to the inputs, that region can be overrepresented in a feature map. What I mean by this is that we cannot effectively choose between the solutions in this region -- the region is small compared to our ability to control inputs. Overrepresenting such a region in a population-based search starves the rest of the problem space of the algorithm's attention.

Thanks for the link, by the way -- this is a particularly interesting application.

I think you confused "evolutionary algorithm" with Simulated Annealing. Because Simulated Annealing is LITERALLY a random walk (biased towards higher values, but its strongly random nonetheless).

https://en.wikipedia.org/wiki/Simulated_annealing

Simulated Annealing works because it doesn't get stuck in local maximums. Do a simple gradient ascent on this simple 2d plot, and its very, very unlikely to achieve the true global value: https://upload.wikimedia.org/wikipedia/commons/d/d5/Hill_Cli...

But if you do Simulated Annealing (aka: random walk), you get to the global maximum much more reliably. Genetic Algorithms have a similar effect as Simulated Annealing because Genetic Algorithms are strongly randomized. But they still "hill climb" explicitly, because its rare for parents to be thrown away if they were superior to the children.

> Simulated Annealing is LITERALLY a random walk (biased towards higher values, but its strongly random nonetheless).

I don't think that's a fair characterisation, and it could be misleading for people new to SA. I'd say SA is a hill-climbing algorithm that has a probability of accepting an inferior move, a probability which shrinks asymptotically over time.

Random walks work well in high dimension soaces, where calculating the gradient gets to be very expensive. Depending on your metaparameters, random walk can also try to jump out of a local minimum, where gradient descent only optimizes within the initial local mimimum.

Absolutely no. Random search does not work in high dimensions because of the "curse of dimensionality" - the number of directions to search grows exponentially with dimension. Gradient descent avoids the problem because it knows the right direction.

Evolutionary strategies are doing (natural) gradient descent using an estimate of the gradient. Here's a good article: http://www.inference.vc/evolutionary-strategies-embarrassing...

Gradient descent can also jump out of a local minimum if the learning rate is large enough, so they're equal in that sense.

But it does make sense that a random walk would be more efficient in very high dimensional problems!

Turing proposed using genetic algorithms to evolve neural networks, and realizing the same issues you have, he then studied those underlying problems more in general (in particular, morphogenesis).

What are your thoughts about Grammatical Evolution? It cleverly uses BNF to constrain the possibilities to legitimate ones.

That was my first thought as well; GE is the easiest way I know of to introduce domain-specific knowledge into the genotype > phenotype translation.

I also thought of that problem and I created language for representing agents in these systems. https://github.com/romankierzkowski/langner Unfortunately, I didn't carry on that work, so I haven't evaluated that kind of representation yet. I would love to know your opinion on Langner.

You know the counter intuitive thing for myself that i found? Sexual reproduction sucks. It doesn't nearly have the performance gains I'd expect. In order to really own the gamut of a solution space, only asexual reproduction really has good consistent results. I've learned a lot more about why that is probably the case now, but i spent a long time getting to the bottom of it.

I think the depends on your genotype-phenotype map. It’s hypothesized that sexual reproduction helps drive modularity because evolution needs to solve the problem of how to bring together two genetically different individuals and still come out with a viable offspring. This is why robustness is so entwined with evolvability. If your genotype-phenotype map is simple then robustness doesn’t need to evolve but this may limit evolvability. The thing I always found with with evolution is that there is no free lunch.

Cliff-hanger! Can you elaborate on why asexual reproduction might perform better?

My theory is that sexual reproduction focuses genes on performance in a stable environment, and asexual reproduction grows to encompass the entire gamut of every survivable environment.

When you don't know the traits that are beneficial in an environment, a significant change adversely affects all sexually reproduced algorithms to a significant degree, whereas it affects all asexually produced algorithms at varying degrees.

>Sexual reproduction sucks.

There is a joke to be made here.

Plants just wanna have fun?

> evolution of evolvability and then a seemingly unrelated subject: the evolution of robustness.

Isn't evolvability only about robustness? What other criteria would improve evolution? Mutations just happen, so the question is how well the phenotype can deal with these mutations. If it can incorporate mutations well, it can tunnel to different useful phenotypes and therefore is robust.

Agree, but it’s not not obvious to most people. Robustness itself if a murky subject: are you robust to mutation, to the environment, genetic noise, genetic background? Also if you are too robust then you can’t evolve. What we want to say with evolvability is that we’re more predisposed positive selection.

Do you think creating a 3D universe (like that being worked on by open.ai), putting the evolvable agents in it and trying to make them evolve learning and intelligence is a good idea?

I mean starting at a very primitive level, you could first aim for something like ant-level intelligence, then go to higher and higher intelligences from there.

I think these type of discussions are really detached from the reality of what neural networks, AI or evolutionary algorithms are.

These are mathematical models that optimise a specific fitness function, they borrow some biological concepts but they are not going to "evolve into ant-level intelligence".

> they are not going to "evolve into ant-level intelligence"

How do we know until we've tried it?

I mean, at a certain level of abstraction, you can just view humans as a bunch of atoms that have been placed in a heat bath for a very long time. And yet intelligence seems to have spontaneously self-organized from it.

Again, this is very detached from what ANNs or genetic algorithms are: relatively simple methods to optimise fitness (or "cost") functions.

They are nowhere close to the complexity of a brain, and certainly do not have enough capacity to be a realistic simulation of the universe.

> How do we know until we've tried it? Tried what, is the question? What's an ant-level intelligence? We have algorithms that can detect images/speech at super-human level (at least in some conditions). Worthy questions, but unrelated to these algorithms.

I'm not sure that ants are doing a "realistic simulation of the universe", though, or anything like it. From what I understand, they're a lot closer to little robots than they are to something that can do Kip Thorne-level astrophysics, or Thomas Aquinas-level philosophy.

I am thinking, in particular, of an experiment done by Richard Feynman where he got ants walking across small sheets of glass. After shuffling the sheets around so the chemical trail became a loop, he was able to get the ants tirelessly marching around in a circle (which they would presumably do until they dropped).

Heck, you probably don't even need an ANN at all to do that, much less an evolutionary one.

You could view it that way, but it won't do you much good, since biology is a long ways from physics, and humans didn't spontaneously emerge from a long standing heat bath.

"humans didn't spontaneously emerge from a long standing heat bath."

They didn't? I'm not sure what you're saying here. Are you arguing intelligent design or something? Because the standard evolutionary theories postulate that humans did, in fact, spontaneously arise from a heat bath (the heat coming from the Sun).

If by "spontaneous", you mean a process taking several billion years which included heat from the sun, but also resources on Earth, with billions of previous ancestral forms from simple replicators to complex multicellular organisms, then okay.

That's not what I typically think of when I hear the word "spontaneous". Spontaneous emergence sounds to me like lightning hitting a swamp over a period of time, and then by a pure chance arrangement of molecules, a fully formed swamp man comes out of it.

That's way different than an evolutionary process.

But that's what the discussion is about here:

> Do you think creating a 3D universe (like that being worked on by open.ai), putting the evolvable agents in it and trying to make them evolve learning and intelligence is a good idea

Basically simulating a universe, seeded with some things we think are already somewhere along the road to intelligence, then seeing if progress is made just by simulating for a long time.

Why am I getting downvoted for this? Evolution isn't spontaneous generation. Evolution disproved any notions of spontaneous generation a century or more ago. All life on Earth evolved via a causal process, not random arrangement of atoms.

Given a fitness function that is the same as an ant's, it seems plausible that they would.

The question is, what is that function?

> Given a fitness function that is the same as an ant's, it seems plausible that they would. > The question is, what is that function?

Not necessarily. Everything points to the fact that the humain brain (at least, I don't know much about ants) does not work in any way similar to neural networks, and as such there's no guarantee that you can represent its behaviour by the current algorithms.

For example, real neurons have no supervision signal. Memory is also a big issue (see the work being done by DeepMind and FAIR on differentiable neural computers, and memory networks) and Reinforcement Learning still struggles with long-term planing, switching strategies, etc.

> Not necessarily. Everything points to the fact that the human brain (at least, I don't know much about ants) does not work in any way similar to neural networks, and as such there's no guarantee that you can represent its behaviour by the current algorithms.

Yes, the brain is most likely not simply modelling one giant ANN, but it's much more plausible that ANN-like components have a role in it.

If there is a space of optimal policies for the ant, then both the ANN and real neural systems will be attracted there. There are certainly a lot of local minima, but it would be surprising to me if ANNs and real neural systems could only find totally disjoint local minima with no similarities.

>and as such there's no guarantee that you can represent its behaviour by the current algorithms.

Doesn't the universal function approximation theorem provide just such a guarantee? It doesn't guarantee any algorithm will converge on that representation, but the capability to represent it is there.

It’s almost certainly not just one function.

An ant’s fitness function is different from its ancestor’s fitness function, which is different from its own ancestor’s fitness function, which is different from...

You don’t get from zero to complexity with a single fitness function.

The question we should ask is: How do we vary the fitness function over time in order to evolve something complex?

I have no idea what the answer is, but...

“If you know the question, you know half.”

-Herb Boyer, geneticist

Balanced Homeostasis? Maybe we could just start with that - it might give an organism life, but it would be interesting to see if social emergent behaviors came from a group of agents optimizing homeostasis alone

You may like this proposal: https://scrollto.com/life-a-universe-simulation/

Open ended evolution is an unsolved problem. You need the right genotype-phenotype map. That’s a hard problem and even if I was lucky and looking in the right direction probably way beyond my IQ.

I assume this is the Peter Eggenberger you are referring to?

https://www.semanticscholar.org/author/Peter-Eggenberger-Hot...

Looks like the same guy.