The Future of Optical Fibre

An anonymous reader writes "An Australian researcher has come up with a novel way of developing optical fibres. Steven Manos, a researcher at the Optical Fibre Technology Centre in Sydney, Australia has developed a method of using genetic algorithims for discovering optimal designs of optical fibres. An article on his work had this to say "The problem with designing optical fibres is starting with a specific set of criteria and then coming up with a design to fit this. The computer program developed by Manos, which is run on supercomputers, does this by mimicking the process of evolution. The computer program combines two patterns to create a third fibre 'offspring', which Manos described as "similar but a bit different". This process is repeated thousands of times with the 10 designs best suited for the particular application chosen to 'breed' again." Another case of "When in doubt, use brute force"?"

Comments

Brute force? Not exactly

[haluness]

I'd rather not think of the method as brute force. Ok, its not like a design from first principles, but its still way to search the parameter space without having to test all coimbinations of parameters

Re:Brute force? Not exactly

[neilmoore67]

I'd rather not think of the method as brute force.

Well said. Brute force would be enumerating every possible optical fibre and then testing them.

This method is more subtle and converges to a close-to-optimal solution with less computer power having to be applied.

Re:Brute force? Not exactly

[N Monkey]

I'd rather not think of the method as brute force.
I'll agree with that. Brute force searching would go though all the parameters a la ..

for(parameter1 = min limit ...)
for(parameter2 = min limit ...)
for(parameter3... )
etc....
Evaluate(parameter1, param2, ....)

Genetic algorithms try to limit the search space by starting with "probably good" sets of parameter values and trying to generate other "probably good but hopefully better" parameter combinations.

It won't necessarily find the absolute best set of parameters but it might find some reasonable ones.

Re:Brute force? Not exactly

[ObsessiveMathsFreak]

I think as this method becomes more popular it will displace the older method of finding the most mathematically perfect solution and designing from that.

In other word instead of fudging designs while you wait ten years for a mathematician to find the equations for the perfect wing, you just get a computer program to 'evolve' one for you. I'll bet this is what boeing and airbus already do.

Sadly this will leave most applied physics mathematicians out of a job. Danm computers!! Taking our jobs and our women!!

PDFs from Manos

[antic]

There are some interesting PDFs of papers co-written by Steven Manos available including these two:

• Novel fibre Bragg grating design using multiobjective evolutionary algorithms
• Optical Fibre Design with Evolutionary Strategies: Computational implementation and results

I'm not going to pretend that I know exactly what's going on, but the first of those two is worth looking at if you have even a passing interest. The second looks to be a little more towards the "deep end".

Much Better

[irokie]

This is a much better example of the application of Genetic Algorithms than the story that was on slashdot the other day (can't find a link, the one about Formula One racing).

in this case they have a very specific set of criteria.

it didn't however mention in the article how they're testing the designs (did it?)...
and are they actually manufacturing any of the designs that have come from thiss yet?

Only on slashdot...

[Mateito]

.. would breeding be regarded as "brute forcing". :)

GA's are not brute force

[jdrugo]

..as they don't search the state space exhaustively. Going through all possible combinations of parameters would be brute force, but in this case, as the parameters are real-valued, this is even impossible (if ignoring the possibility of quantisation)

Evolutionary Algorithms provide informed search as they perform competition among the individuals (each representing one possible solution) in the population. Their performance is way above exhaustive search techniques (which _are_ brute force) but below classical search techniques. In this case, however, such classical techniques cannot be applied as the problem space is not well-defined.

No, Taco, No

[neoshroom]

Another case of "When in doubt, use brute force"?"

No, Taco, No.

From the 'brute force' entry in Wikipedia:

In computer science, Brute Force, sometimes called the Naive Method, is a term used to refer to the simplest, most intuitive, most spontaneous, and usually most inefficient methods of accomplishing a task.

This is exactly what a genetic algorhthem is not. If you have a million numbers brute force would be to go from the first to the last in order. Using a genetic algorhythem provides a shortcut though Design Space wherein you need to try far fewer combinations in order to come to a successful result.

C'mon Taco, of all people, you should know this!

An Australian Genetic algorithm?

[Timesprout]

With all the weirdo animals the Australian continent has produced I guess this program will produce some highly interesting results. I cant wait for the announcement that a pattern resembling a Duck Billed Platypus is ideal for streaming Digital TV.

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[account_deleted]

Comment removed based on user account deletion

Brute force? No way?

[carldot67]

Genetic algorithms are computational shortcuts that are used to very quickly find minima in complex multiparameter functions.

Suppose you wanted to find the lowest value of f(x)=sin(x) where x is from 0-360. (OK we all know its at x=270 but hear me out) - you can do it a couple of ways:

1. calculate sin(x) for all 360 possible values of "x" or
2. calculate sin(x) for (say) 20 values of "x".

Statistics says approach 2 will give you a couple of promising results, for only 1/18th of the effort. Now "breed" another 20 from the 6 values of x for which sin(x) were lowest, say 190, 210, 212, 260, 278, 290. This "next generation" gives sin(x) values whiach are closer to zero. Take the best 6 again. After three generations you are *close* to finding the values for "x" that give you sin(x)=0.

So systematic examination takes 360 tries and the genetic shortcut takes 60 tries - about 17% of the computational effort.

Now imagine a function a bit more complex; some mad multivariate affair like the wave equation. Each variable becomes a "gene" in the above "breeding program". All the time we are looking for parents and offspring that *tend* towards the answer we are looking for. (We also chuck in some unrelated parents too, since inbreeding can be bad - a tip stolen from Monte Carlo techniques [which see]).
The computational savings from GA, GP and MC techniques are potentially huge (as in orders of magnitude) so long as you dont care that:

a) The answer is not 100% exact
b) Some alternative minima are missed

Genetic Algorithm + Hill climbing

[G4from128k]

For exploring real-valued phase spaces, one solution is to combine a GA with a classical hill-climber. A hill-climber evaluates the local gradient (the partial derivatives of fitness with respect to the independent variables) and then makes a directed adjustment of the solution in the direction of better performance. Hillclimbers can reach optima in floating-point spaces very quickly, but tend to get stuck on local solutions.

GAs are great for jumping out of local optima to find new realms of the solution space, but don't converge as quickly on the neighborhood optima. So the combination of a GA with more classical optimzation can work well.

this guy is way too confident

[kjba]

No other algorithm can come up with a design for optical fibres that are cheap to make and transmit data at a high rate, Manos said.

How can anyone make a claim like this? Just the fact that one can't think of any other algorithm doesn't mean no such algorithm exists. For many problems that can be solved by genetic algorithms, other (problem-specific) algorithms exists (or may exists) that are way more efficient. The nice thing about genetic algorithms is that it is a standard tool that often works, not that it is an exceptionally smart way of doing things.

Re:this guy is way too confident

[geeber]

Well, actually lot's of algorithms exist for designing optical fiber, and they do it efficiently and very accurately. I use a number of in house proprietary programs for designing optical fibers all the time. And I can tell you we don't waste time messing around with GA's

So why don't you hear a great deal about such algorithms? Well, for one, they don't have cool names like "Genetic Algorithms". Also, they are highly prized and considered extremely valuable intellectual property for the companies that actually make optical fiber. We are not going to publicise all the details the most fundamental design tools of our business.

GA's are not the future of optical fiber. They are, however, excellent for generating academic papers, which in turn are highly useful for getting tenure.

What's so great about meat?

[szquirrel]

This process is repeated thousands of times with the 10 designs best suited for the particular application chosen to 'breed' again." Another case of "When in doubt, use brute force"?

More like another case of computer science being fascinated by meat.

Remember when neural networks were the next big thing? Everyone was applying them to everything, whether or not it made sense to solve the problem that way. It's neural! Just like our brains! Our brains are smart, they will make our computers smart!

I'm sure genetic algorithms will eke out a useful place in the computer science toolkit, I just doubt it will be as broad as the current fashion of applying them to everything from optical fiber to race cars to compilers.