An Amoeba-Based Computer Found Solutions To 8-City Traveling Salesman Problem (vice.com)

dmoberhaus shares a report from Motherboard: A team of Japanese researchers from Keio University in Tokyo have demonstrated that an amoeba is capable of generating approximate solutions to a remarkably difficult math problem known as the "traveling salesman problem." The traveling salesman problem goes like this: Given an arbitrary number of cities and the distances between them, what is the shortest route a salesman can take that visits each city and returns to the salesman's city of origin. As these Japanese researchers demonstrated, a certain type of amoeba can be used to calculate nearly optimal solutions to the traveling salesman problem for up to eight cities. Even more remarkably, the amount of time it takes the amoeba to reach these nearly optimal solutions grows linearly, even though the number of possible solutions increases exponentially. The reason this amoeba is considered especially useful in biological computing is because it can extend various regions of its body to find the most efficient way to a food source and hates light.

To turn this natural feeding mechanism into a computer, the Japanese researcher placed the amoeba on a special plate that had 64 channels that it could extend its body into. This plate is then placed on top of a nutrient rich medium. The amoeba tries to extend its body to cover as much of the plate as possible and soak up the nutrients. Yet each channel in the plate can be illuminated, which causes the light-averse amoeba to retract from that channel. To model the traveling salesman problem, each of the 64 channels on the plate was assigned a city code between A and H, in addition to a number from 1 to 8 that indicates the order of the cities. To guide the amoeba toward a solution to the traveling salesman problem, the researchers used a neural network that would incorporate data about the amoeba's current position and distance between the cities to light up certain channels. The neural network was designed such that cities with greater distances between them are more likely to be illuminated than channels that are not. When the algorithm manipulates the chip that the amoeba is on it is basically coaxing it into taking forms that represent approximate solutions to the traveling salesman problem.

Comments

The true solution, or a usable solution?

[ctilsie242]

With genetic algorithms, you can come up with a solution in linear time (as in 100 seconds for 100 cities, 200 seconds for 200 cities, etc.) that is "good enough". It won't come out with the best one, proven mathematically, but if you are looking for a useful answer rather than _the_ answer, it works.

This work with the amoeba seems like it can give a passable solution, but it would be interesting if it did give the actual shortest out there.

Re:The true solution, or a usable solution?

[rtb61]

The computational trick is, it is not about going from one city to another, it is all about the journey itself and what route it takes. So you break up the journey from city to city, into smaller segments along that route and test those smaller segments, with the size of the segments defining accuracy or in journey terms, main intersections along that route. Although it sounds like you are creating more elements to analyse, what you are really doing is analysing the actual route rather than the destinations because that is what the problem is about the route and not the destinations.

Re: The true solution, or a usable solution?

[phantomfive]

The traveling salesman problem doesn't need an estimate, we can find a correct solution, and it is easily parallelizable. If you have many processors (or amoebas acting like processors), you can find it easily. The trick here was training amoebas, not solving the problem.

Re:The true solution, or a usable solution?

[ShanghaiBill]

There are tons of simple heuristics for quickly getting "good enough" solutions to optimization problems. So that is not interesting at all, and is not "solving" the TSP. To solve the TSP means to get the absolute shortest path. An amoeba based computer can't do that in polynomial time.

Yeah, that's impressive and all

[rsilvergun]

but can it run Crysis?

Re:Yeah, that's impressive and all

[quenda]

Crysis?
I would have gone with "I, for one, welcome our new Amoeba overlords."

Re:Yeah, that's impressive and all

[TheGratefulNet]

amateurs.

I have netbsd running on my 'meba cluster.

(systemd-free, too, mind you)

Re:Yeah, that's impressive and all

[dgatwood]

but can it run Crysis?

In general, no. Steam kills Amoebas, because boiling water is too hot for them.

That said, by lowering the ambient air pressure, you can make water boil at a lower temperature. Amoebas can survive sustained temperatures of 46 C. An online calculator tells me that water boils at 46C at .11 bar, and another one says that .11 bar is the air pressure at about 51,000 feet above sea level.

Of course, merely being able to survive Steam may still not be sufficient to run Crysis. To determine that with certainty, we need to devise a proper experiment.

Anybody have an SR-71 handy? We need to test this theory. This important question demands an answer.

Right

[Dunbal]

So basically - we designed a method to make an amoeba respond in the way we wanted, then lo and behold, the amoeba - when "coaxed" by our model, responded the way we wanted... It's a miracle I tell you.

Re:Remarkably difficult

[ShanghaiBill]

The travelling salesman problem is not difficult if you're willing to settle for "approximate solutions".

As a general rule, solving most problems is not difficult if you don't actually solve them.

Can you imagine...

[sacrilicious]

...a Beowulf cluster of these?

Obligatory xkcd quotes

[LordHighExecutioner]

There is an O(1) solution to this NP-complete problem.

The Operating Syatem.

[Cmdln Daco]

From the title, I thought maybe this was about the Amoeba Operating System . No such luch, alas.