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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"?"
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
Another case of "When in doubt, use brute force"?
Evolutionary search isn't "brute force", you id... At least not for meaningful definitions of 'brute force'
Brute force would be starting at one end of design space and evaluating each design in turn.
Belief is the currency of delusion.
..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.
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!
Big apple, new Yorik, undig it, something's unrotting in Edenmark.
You may be trolling, but I'll point it out anyway: evolution doesn't have an end point. It is a path - a path with many branches and dead-ends - and not a destination. And I'm not being all metaphysical and hippy-ish about it.
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.
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.
Never approach a vast undertaking with a half-vast plan.
The problem of local minima is often significant. A good analogy is real genetics - each species has evolved into a "local minima" for likelihood of extinction. If the wings on a given type of butterfly become slightly larger or smaller there will typically be a survivability penalty of some kind, and wing size has stabilized at the optimum for that species. But look at the difference in possible local minima: in one case it results in a whale, and in another it results in toenail fungus. Neither could survive if suddenly given some of the characteristics of the other. A beautiful orchid is a local minimum, and so is pond slime. Your genetic algorithm could decide that pond slime is the optimal product, and the difference can impact your ornamental plant business due to subjective things like beauty that can't be quantified.