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Math Software Science Technology

An Amoeba-Based Computer Found Solutions To 8-City Traveling Salesman Problem (vice.com) 87

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.

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An Amoeba-Based Computer Found Solutions To 8-City Traveling Salesman Problem

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  • by ctilsie242 ( 4841247 ) on Friday December 21, 2018 @09:14PM (#57844528)

    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.

    • This probably isn't much different than ant colony solutions, which have been studied for quite a while [sciencedirect.com]. The results aren't perfect, so it's not a true solution, but I think the experimental results show that the solutions tend to be close enough to ideal to be useful.
    • by rtb61 ( 674572 )

      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

      • by ls671 ( 1122017 )

        The problem with what you suggest is that there doesn't seem to be any route (or road) in the description of the problem linked in TFA. Imagine you use an airplane. So, it seems like keeping a straight line between cities is optimal.
        https://en.wikipedia.org/wiki/... [wikipedia.org]

        Otherwise, like in real life where there are roads, you would have a point.

    • 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.
  • by rsilvergun ( 571051 ) on Friday December 21, 2018 @09:28PM (#57844574)
    but can it run Crysis?
    • by quenda ( 644621 )

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

    • by TheGratefulNet ( 143330 ) on Friday December 21, 2018 @10:01PM (#57844640)

      amateurs.

      I have netbsd running on my 'meba cluster.

      (systemd-free, too, mind you)

    • by dgatwood ( 11270 ) on Friday December 21, 2018 @11:56PM (#57844862) Homepage Journal

      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.

      • 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.

        They didn't survive Jack Tramiel.

    • That joke is too last decade to be funny anymore. At least to me.
    • by dohzer ( 867770 )

      No, but it can create a one in the hospital system.

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

    • by ShanghaiBill ( 739463 ) on Friday December 21, 2018 @11:08PM (#57844768)

      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.

    • The "close to optimal" algorithm is definitely not simple. But of course you could try randomly millions of paths and take the best - which could still be very far from optimal.
    • My solution to the traveling salesman problem is to just shoot them when they ring the doorbell. Problem solved. Iâ(TM)m US; I suspect the Brits would rather use a knife because gun violence is bad. Iâ(TM)m surprised the Japanese are using amoebas - I thought swords would be more their style. If we all get to work, however, we can solve the traveling salesman problem once and forever, just like polio.
  • If u dont mind a little Slime. :)
  • Right (Score:4, Insightful)

    by Dunbal ( 464142 ) * on Friday December 21, 2018 @11:06PM (#57844760)
    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.
    • Yeah, I don't get it either. Sounds like a complete waste of time. The computer solved the problem and was based on digital electronics not the amoeba. It controlled an amoeba. You could do the same experiment with mice, or ants, or people, or whatever.
    • by eriks ( 31863 )

      Yeah, that was my first though too, since the summary makes it sound that way, but the article explains in depth:

      The challenge for the plasmodium to find the shortest tour is that its branches should not enter the frequently illuminated lanes and should elongate into the optimal combination of the least frequently illuminated lanes. However, the optimal combination cannot be found as long as the branches always obey the control principle; if always the branches shrank when illuminated and expanded when not illuminated, the plasmodium would not avoid falling into a local minimum. To better explore the potential energy landscape and locate the global minimum (the shortest tour), the plasmodium needs to misallocate the resource to its branches and the branches must violate the control principle with a certain small probability, because the lengths of the tours can be compared only when the branches operate contrary to their photoavoidance response

      so the computer is only really defining where the "bounds" of the problem are. The ameoba really is doing the computational work (going *against* the computer's control) to find a nearly-optimal solution.

      • by Dunbal ( 464142 ) *
        Yeah I could do the same thing with water and valves, with the closed valves "encouraging" the water to flow elsewhere and the open valves "encouraging" the water to flow in that section and look, I just invented the Traveling Salesman Problem solving puddle.
        • by eriks ( 31863 )

          Not quite... since the water and valves could (I think) only find a "local minimum" or naive solution, not a near-optimal (global minimum) one, like the amoeba does. The lighted areas on the chip don't *prevent* the ameoba from going there, and in fact to find an approximate global minimum solution, the ameoba *has* to sometimes go where it doesn't want to, in order to maximize it's nutrient intake. In other words the water can't "decide" to go through a closed valve, but the ameoba can choose to extend i

      • My head hurts. There are 8 "cities" represented by letters A-H. Each city has 8 channels. The amoeba selected channel C1 (C is the first stop) .. B8 (B is the last stop). Since the researchers didn't actually coax that path, how did their relative light pulses work? Did channels B1 through B8 all equally have the most light when compared to the light pulses in C1 through C8? If so, how do they determine that B should be the last stop? If not, if rather all the light impulses across all 64 channels ra
    • So the amoeba is just a display? I wondered if I'd missed something when I read it.

      Then I asked myself why it was even posted, since it's not really a story, is it?

      And then I remembered where I was.

  • by sacrilicious ( 316896 ) <qbgfynfu.opt@recursor.net> on Saturday December 22, 2018 @12:08AM (#57844882) Homepage
    ...a Beowulf cluster of these?
  • "4-Dimensional Bags of Mostly Water Evolve Sentience and Create An Amoeba-Based Computer Which Found Solutions To 8-City Traveling Salesman Problem"
  • by LordHighExecutioner ( 4245243 ) on Saturday December 22, 2018 @02:55AM (#57845142)
    There is an O(1) solution [xkcd.com] to this NP-complete [xkcd.com] problem.
  • From the title, I thought maybe this was about the Amoeba Operating System [cs.vu.nl]. No such luch, alas.

  • The challenge of the Traveling Salesman Problem is the combinations. The amoeba is trying all solutions at once, and the light is telling it where not to go. The real question is, "Given the preconditions, and the behavior of an amoeba, could it not create an approximate solution?" Clearly not.

  • So we can now replace salemen with amoebas? I might even listen to an amoeba.

  • A paramecium and an amoeba are walking down the street. The amoeba asks “So, lacking any pseudopodia, how do you manage to get around? The paramecium replies “A cilia question I’ve never heard!”
    -----
    Why did the amoeba flunk the math test?

    Because it multiplied by dividing.

  • Hm, is the intelligent part the amoeba or actually the neural network coaxing them towards the solution?

  • ...but it largely shied away from the limelight and never gobbled up much market share.

    I thought Slashdot just posted its annual Amoeba elegy post last week.

    (Sorry.)

It is wrong always, everywhere and for everyone to believe anything upon insufficient evidence. - W. K. Clifford, British philosopher, circa 1876

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