Follow Slashdot blog updates by subscribing to our blog RSS feed

 


Forgot your password?
Close
typodupeerror
×
Software Science Technology

Breeding Race Cars With Genetic Algorithms 187

Posted by timothy from the inbreeding-isn't-just-for-the-cheap-seats dept.
smack-pot writes "Wired News has an article about how the Digital Biology Interest Group at University College, London is using genetic algorithms to breed superfast Formula-One race cars. 68 design parameters were configurable in the cars, and the generated designs were tested using the racing simulation software developed by the game developer Electronic Arts. According to the research it is possible to shave off 88/100th of a second per lap by using genetic algorithms to tune the cars. In an industry where a tiny fraction of a second matters, that's significant."
This discussion has been archived. No new comments can be posted.

Breeding Race Cars With Genetic Algorithms More

Breeding Race Cars With Genetic Algorithms

Comments Filter:

  • Wow! (Score:5, Funny)

    by anethema ( 99553 ) on Monday June 21, 2004 @04:29AM (#9482566) Homepage
    This will EVOLUTIONALIZE racing ;)

    • Re:Wow! (Score:1, Funny)

      by Lars T. ( 470328 )
      No way! God created all racecars on a Sunday 5987 years ago!

  • Genetic algorithms explained (Score:5, Informative)

    by ArbiterOne ( 715233 ) on Monday June 21, 2004 @04:31AM (#9482578) Homepage
    Here's a good link [mit.edu] for people who don't know what genetic algorithms are:

  • It should be noted that... (Score:5, Informative)

    by mOoZik ( 698544 ) on Monday June 21, 2004 @04:32AM (#9482582) Homepage
    It should be noted that the "research" was done with a video game and no actual tests have been conducted on real cars and situations. This does not mean the techniques cannot be applied in real situations, but just that it has not been done yet.

    • Re:It should be noted that... (Score:5, Informative)

      by Analogy Man ( 601298 ) on Monday June 21, 2004 @04:40AM (#9482599)
      This is a very good point. From my experience optimization algorithms are very powerful tools for finding weaknesses in simulations. Using genetic algorithms to optimize wings for supersonic aircraft I ran into some "interesting" solutions. The boundary layer algorithm did not do a very good job of predicting seperation so it over worked some areas of the design beyond what physically would work.

      This is not to say that this is not a very powerful tool for complex design spaces. If your design space is not particularly interesting (few localized optimums) gradient methods are more intuitive and efficient.

    • Actually it isn't even that. The teams are usually very secretive about how they optimize their cars, so nobody knows whether they use GAs or not.
    • Genetic Algorithms, or similar techniques, are probably used by some teams. Most teams have a huge database of the various setups used in the past. That data could be used to feed a complex model of a car's performance for a given driver. The models must be tuned to a given driver, as drivers have strong preferences for how their cars are tuned.
    • It should be noted that the "research" was done with a video game and no actual tests have been conducted on real cars and situations. This does not mean the techniques cannot be applied in real situations, but just that it has not been done yet.

      Absolutely true. I work with evolutionary algorithms all day, and the fact of the matter is that simulations are notoriously inaccurate when it comes to predicting real-world behaviour. Fine-tuning 68 parameters with an evolutionary algorithm is not that difficult

  • So (Score:3, Funny)

    by Anonymous Coward on Monday June 21, 2004 @04:35AM (#9482586)
    when a driver slams into the wall, will this be a GMO accident?
    • I'm not sure what a GMO accident is, but I think a crash with a single fatality could win two darwin awards.

  • Pedigree (Score:5, Funny)

    by Ratface ( 21117 ) on Monday June 21, 2004 @04:43AM (#9482604) Homepage Journal
    How long then before racecars come with a "pedigree" like a champion racehorse or a Crufts prizewinning pooch?

    "And Schumacher rides to victory again in his car 'Victorious Monarch' which of course comes from the Ferrari stable and is the offspring of 'Burning Rubber' and 'Teutonic Speed Demon'"

    • "Thanks to an initially nasty mutation which smoothed out in the 38th generation, we found that the 'Powered by Stickers' sticker was causing undue shear."

  • Mutant cars (Score:5, Funny)

    by Zog The Undeniable ( 632031 ) on Monday June 21, 2004 @04:51AM (#9482625)
    This lot [uglycars.co.uk] must have come from one of those places where it's still legal to marry your sister...

  • Slow moving (Score:5, Interesting)

    by pubjames ( 468013 ) on Monday June 21, 2004 @04:54AM (#9482633)

    I did some research and programming in this field over a decade ago. The really frustrating thing about this field is how slow moving it is and how little it is taken seriously.

    When you have constructed an environment and electronic "organisms" that can breed within that environment, and then watched the generations gradually improve and adapt to the environment, you get the feeling of a new kind of power that we haven't really tapped yet - evolution.

    I think one of the problems is that people don't get what is happening in these types of projects. When I showed people the projects I was working on - even biologists and computer scientists - the first reaction was that what they were seeing was just a simulation - i.e. that I had programmed in the fact that the organisms adapted to the environment. It took a lot of explaining to convince some people that what they were seeing was actual evolution, albeit in digital form.

    The fact that this research is just looking at breeding cars which are used in a computer game just demonstrates how slow moving developments in this area are. Evolution could be used to improve many aspects of cars -- their engineering, efficiency, production and even visual design. It will happen one day, but it's taking us a hell of a time to realise that we can exploit the force that produced all the wonderful things we see in nature.

    • Re:Slow moving (Score:4, Insightful)

      by Pooua ( 265915 ) on Monday June 21, 2004 @05:36AM (#9482721) Homepage
      The really frustrating thing about this field is how slow moving it is and how little it is taken seriously.

      It is difficult to take seriously a field that is advocated by people possessing more of an idealistic agenda than a pragmatic demonstration of benefits. AI in general suffers from this problem. In the case of GA, some people insist on using the technique as an argument advocating biological evolution, even though 1) it bears only a vague relationship to biological evolution and 2) is just another tool out of many tools, not the be-all-end-all that proponents want to present.

      • > In the case of GA, some people insist on using the technique as an argument advocating biological evolution

        How can you "advocate" biological evolution? That's like saying people researching meteorology are "advocating" fluid dynamics.

        People in GA might be trying to *imitate* biological evolution, which sounds like a good idea, seeing how evolution has created some of the most amazing machines and materials on earth.

        • Re:Slow moving (Score:2, Insightful)

          by hopews ( 450546 )
          I believe by "advocating biological evolution", he means show evidence that evolution is the driving force behind the biosphere on earth. There are still many who contend that evolution is not sufficient to produce all of the creatures we share this rock with. To renouce this, some say that if digital evolution can make strange digital creatures suited to their digital ecosystems, evolution can do so in the world as well. I don't think its a very strong argument though.
          • It's more of an illustration. Anyway, I would have thought that the computer scientist were hoping that the analogy with the real world would convince people their algorithms are a good idea, rather than the other way round!

            I can't see how anyone with even a small insight into biochemistry can doubt the biodiversity on earth came about through evolution...
        • How can you "advocate" biological evolution?

          - Claim that "evolution has created some of the most amazing machines and materials on earth."

          - Claim that evolution is a fact.

          - Pour millions of dollars in tax money into required classes that teach that evolution is a fact.

          - Name a computerized method of selection and optimization with a name that implies or suggests that it is similar to biological evolution.

          That's like saying people researching meteorology are "advocating" fluid dynamics.

          A b

          • > - Claim that "evolution has created some of the most amazing
            > machines and materials on earth."

            You find me an engineer that can build a robot the size and weight of a spider that can navigate a forest autonomously. Of course you might have different ideas of what is "amazing" but it's pretty clear that evolution has created somethings humans have not yet been able to copy.

            > - Claim that evolution is a fact.

            It's a process, not a fact. This process is responsible for the biodiversity on earth, t
    • Maybe you can answer my perennial question about this -- it seems to me that genetic algorithms are conceptually straightforward, and that the really difficult/interesting part is how you map your "genome" to relevant parameters in your problem space. It's important that mutation and, especially, recombination should lead to "organisms" which really do resemble their "parents", otherwise you lose the whole power of the approach.

      I'm not active in the field, but I've read some books and papers on the topic,

  • Breeding cars... (Score:5, Funny)

    by pyrrhonist ( 701154 ) on Monday June 21, 2004 @04:55AM (#9482636)
    If you're like me, you're probably wondering how they breed cars.
    After careful research, I found a visual aid [amazon.com] that helps clear up the mystery.

    **WARNING** Do not view at work (if you are a mechanic). It's a truckse.cx link.

  • From the mouth of one in Formula SAE (Score:5, Informative)

    by Peden ( 753161 ) on Monday June 21, 2004 @04:57AM (#9482641) Homepage
    As a member of a raceteam which is about to enter the formula SAE competition. (A global university based competition aimed at building the fastest racecar) I find that 68 parameters are not nearly enough. Modern racecars have that many in the suspension alone. And all those phony calculation with determination of how many seconds are spared cannot be used for anything concrete.

  • Difference between simulation and reality (Score:5, Insightful)

    by Minimind ( 755367 ) on Monday June 21, 2004 @04:59AM (#9482644) Homepage
    There is a large difference in evolved behaviour between physical things and models of those same things. GAs using physics simulators are very good at exploiting inaccuracies and subtle features of the simulation, making the transfer between the simulation to reality very difficult without the use of specialised techniques such as Minimal Simulations [psu.edu] and Incremental Evolution.

    This means you have to be skeptical with experiments performed just in simulation without testing the same model in reality.

    • There is a large difference in evolved behaviour between physical things and models of those same things.

      Surely that just means your physical model of the real world is not correct?
      • Surely that just means your physical model of the real world is not correct?

        I can't believe that such a comment would apply to an "Electronic Arts" video game...

      • Yes, that's right. No model of the physical world is 'correct', although some may be more so than others.

        Building robots or complex physical things whose attributes were evolved in a physics simulation such that their behaviour is more or less the same (the 'reality-transfer' problem) is difficult and an active area of research in evolutionary robotics.

      • Surely that just means your physical model of the real world is not correct?

        Whether it's correct or not is irrelevant, if the machine you are using to do the simulation cannot carry out the calculations with sufficient precision to avoid exponentially diverging from reality (otherwise known as "chaos").

        Perfectly simulating reality is impossible. This statement has not been proven, but I firmly believe it, along with a multitude of other people who are quite adept at simulation methods.

        Hence, the orig

    • I was thinking about another kind of Minimal Simulation... [eznet.net]

      No, seriously... using a smaller "universe" so they can test "real-world" while still using only 68 parameters (sic)...

      And have all the car computer controlled, for testing, you know... + some serious fun 8)

  • Differential evolution (Score:2, Interesting)

    by 12357bd ( 686909 )

    One of the classical algorithms to do genetic evolution using floating point values (not bits) as parameters, is Differential evolution. [berkeley.edu]

  • GA example, (Score:3, Interesting)

    by noselasd ( 594905 ) on Monday June 21, 2004 @05:25AM (#9482694)
    Reminds me, I made this [dyndns.org],
    which is some very simple code for the uninitated to genetic algorithms.

  • Human competitive problem solving (Score:5, Informative)

    by NoOneInParticular ( 221808 ) on Monday June 21, 2004 @05:28AM (#9482700)
    Next Genetic and Evolutionary Computation COnference [uiuc.edu] in Seattle starting next week will have a special session focussed on Human Competitive Results [uiuc.edu] obtained with evolutionary algorithms. In recent years, a number of results have been obtained with evolutionary computation that equal or exceed the performance of dedicated individuals applying itself to the task. One I saw recently is that with genetic programming a satellite antenna [spaceref.com] was designed that hopefully will gets its launch next January. Genetic Programming is also used to create quantum programs [hampshire.edu], a task humans have great difficulty with. There are a number of such results.

    Interestingly enough, Peter Bentley's group results on car racing would not be considered human competitive, unless the results obtained in the simulation will be tried in the real world, or if the simulator is something experts actually use to shave of seconds. In any case, it seems the Evolutionary Computation world is starting to obtain very strong results, for a part due to Moore's law. It's possible that this is caused by the fact that the field simply tries to solve things, instead of first proving that it works (AI/ML), or proving that it doesn't work (Operations Research).

  • Genetic Algorithms, Rat Bags and Cheetahs. (Score:5, Informative)

    by falsemover ( 190073 ) on Monday June 21, 2004 @05:34AM (#9482718)
    Ok, having done a lot of work in Genetic Algorithms here is the elevator pitch.

    A genetic algorithm is an algorithm that manipulates encoded problem solutions using a population of potential solutions. Each solution, or population member, in this case, is a set of racing car parameters. The genetic algorithm selects a couple of solutions and recombines parts of each to produce two new solutions using a recombination operator. Mutuation is normally added as well. The two new solutions are then "measured" for fitness; in the racing scenario a full scale simulation of the actual car is carried out. This produces a single value of fitness that is associated with the newly generated member.

    The algorithm proceeds by selecting a couple of candidate parents; normally with random bias weighted toward fitter parents. The parents mate, new chidren produced, the children are measured, then integrated back into the population and they cycle continues.

    The end result of all of this is that small "above average" solution components "accumulate" in the population at an exponential rate as time goes on. Of course, this only happens early in the first few generations before high "saturation" / convergence levels are reached. This is kind of cool because something good is happening at an exponential rate as time goes on; this is very useful when trying to solve problems with vast state spaces; eg the problem of finding a good racing car model where you need strong brew to find a resonable solution. Later on, most of the population members can often encode very fit solutions. This mathematical property (exponential accumulation) explains why the genetic algorithm is the algorithm of choice in nature, and also why an alarming proportion of PhD students are now studying genetic algorithms. This technique isn't new either, as Ratbag games have been using these techniques and other cool machine learning techniques for years to evolve the AI on their car titles such as "Dirt Track Racing" and "Powerslide".

    Of course, we already know that this stuff works; as a quick trip to the zoo will show you what evolution has done to optimize the cheetah.

    This is a very simplified view; there are a bunch of design issues such as encoding, premature convergence, crossover (recomination), reproduction methods, method of generation, population sizing, operator adaptation that make this whole field very interesting and addictive. Having written a dozen genetic algorithms and solved many many problem types using GAs they never cease to suprise me how powerful these methods are.

    • Having written a dozen genetic algorithms and solved many many problem types using GAs they never cease to suprise me how powerful these methods are.

      I work in this field too:
      I remember some years ago, talking with a coleage, about neural networks, I told him that i was using genetic algorithms for a) select suitable initial conexion values, and b) help to scape local minima.
      He as surprised that both methods could succesfully cooperate. :)

    • This mathematical property (exponential accumulation) explains why the genetic algorithm is the algorithm of choice in nature

      What a misguided statement. Nature is not self-aware and it does not make "choices." There was not some moment in the past where the universe decided, "Hey, I'm going to implement evolution, because that's the best algorithm for creating life."

      Evolution is a tautology. It essentially states, "Those individuals who survive, are the ones who survive." Really, that's all it boils do

      • Interesting way to paraphrase it, but unfortunately hopelessly wrong. Natural selection is not about what survives, that is totally irrelevant. What reproduces is what it's about. There's still a tautology lurking there, granted: the one that reproduces best will have the most offspring. Still, also this needs some extra qualification. Reproduction alone is not enough: the children themselves need to be able to reproduce, otherwise reproduction is again a dead end street (creating only sterile offspring wil

    • Of course, we already know that this stuff works; as a quick trip to the zoo will show you what evolution has done to optimize the cheetah.

      After several million years, the best that nature has come up with can do about 70 mph for short periods. Human rally cars can sustain higher speeds for longer over the same terrain.

      There are many cases were evolution has led to sub-optimal "designs" (the connection of the retina to the optic nerve in the human eye being one that springs to mind).

    • "here is the elevator pitch"

      Goddess... Remind me not to get onto a elevator with you.

  • what it shows ... (Score:5, Insightful)

    by curator_thew ( 778098 ) on Monday June 21, 2004 @05:39AM (#9482725)

    Is that genetic algorithms are nice for parametric optimisation, but not for breakthrough innovation.
  • The "Wired" article is just a breathless piece of evolution worship, lacking useful, critical or practical information. This is particularly apparent in the paragraph that states,

    "Using this sort of programmed procreation, the Digital Biology Interest Group has made self-healing battlefield surveillance robots -- gadgets that look like robotic snakes that can figure out how to wiggle home even when severely damaged, unlike less-evolved robots that typically just give up when one of their critical compone

  • Human Error (Score:2, Interesting)

    by (eternal_software) ( 233207 )
    88/100th of a second per lap?

    Isn't that well within the margin of error that the human drivers would introduce? If the driver takes a turn slightly off of the most optimized route, wouldn't that negate the fraction of a second these algorithms are providing?

    • Re:Human Error (Score:5, Interesting)

      by Johan Veenstra ( 61679 ) on Monday June 21, 2004 @06:25AM (#9482818)
      - 0.88 seconds is not well within the margin of error that the human drivers would introduce.

      - If you would put all 20 current f1 drivers in exactly the same car, 15 of them would qualify within 0.5 of a second.

      - 0.88 seconds advantage every 73 laps (Indianapolis) would accumulate to 64,24 seconds (almost a lap).

    • Re:Human Error (Score:4, Insightful)

      by achurch ( 201270 ) on Monday June 21, 2004 @06:29AM (#9482832) Homepage

      88/100th of a second per lap? Isn't that well within the margin of error that the human drivers would introduce?

      Yes, but that doesn't negate its value (assuming the measurement is viable on the physical racetrack and not just in simulations). If you have a normal die A with sides labeled from 1 to 6 and another die B with sides labeled from 2 to 7, then there will certainly be rolls where A is higher than B, but on average, B will roll higher than A. In racing, this would translate to a slightly greater chance of winning--and while that may not be a breakthrough improvement, it's certainly better than none at all.

  • Genetic Racing (Score:5, Funny)

    by Moblaster ( 521614 ) on Monday June 21, 2004 @06:45AM (#9482868)
    Genetic Racing sounds great in theory, but wait until the first inbred cars come out. You know, they start all scientific with that Formula 1, but when it works its way deep inside the country with NASCAR... oh, my hominy grits... those Republicans are gonna want to force us to race whatever comes out of the oven.

  • 750/1000 GUARANTEED (Score:3, Funny)

    by Anonymous Coward on Monday June 21, 2004 @06:50AM (#9482882)
    Zen is certainly the mystical function of the equation. Unfortunately, it is one we engineers find difficult to address.

    In my many years of study (I go almost all the way back to Prolifferro Nuvolari), the theme of the driver as a closed loop has been my frame of reference. At speed the human body supplies an enormous amount of sensory data from vibration, centrifugal force acting upon the entire body, visual, auditory, data from the parts of the body in direct contact with the car, etc. etc.

    That data combines within the nervous system and results in a tremendously complex firing of nerves that initiate hundreds of thousands of muscle twitches and jerks that, when applied to the controls of the car, make it go around. I know this sounds complex but when you realize we are dealing with thousandths of a second per lap, you'll see what I mean.

    "There must be a better way", I always said to my self. Then one hot summer day, while eating a Creamcicle, it came to me. "The parts of the body in direct contact with the car" !! Carumba!!! Why didn't I think of it before ?? And, which part of the body has the greatest surface area that contacts the car ?? It was as plain as the nose on your face.

    You will appreciate the need for working in secrecy these last few years. But, since you brought it up, now it can be told. If I could come to you as a race car driver and say, "How would you like to have 750 ONE THOUSANDTHS of a second per lap, guaranteed, money back, for only $89.95." What do you think you'd say ? Think of it. That's 562,000 one thousandths in the Sunbank 24 hours or almost 10 minutes !!

    It took several years to develop and test my theory. My methods shall go with me to the grave. I was able to ascertain that there is a direct correlation between the sensitivity of a race car drivers Glutinous Maximus and his standings in his respective series. Then the question became, "How to neutralize this God given "Unfair Advantage" ?? How to give those less well endowed by their makers a boost up, so to speak, in this department ?? It was an ergonometrict engineering tour de force.

    Sometimes the old ideas are best. Do you remember the old "Union Suit" ? With the trap door ? My Company has developed (with clever use of Velcro and tiny Japanese electric motors) the "Tenth of a Second" driver's suit. We advertise 750/1000 but
    actually deliver a full tenth.

    The device is simplicity itself. When the driver squirms down into the car, our unit pulls away all 3 layers of cloth rolling them neatly into an out of the way pouch. This puts the actual skin of the driver's Ass in direct contact with the Kevlar of the car seat. When the driver pulls himself up out of the car, the device modestly reverses, the result being seamless and unobtrusive. A special crash sensor activates the device in that eventuality, preventing possible burns. There is a separate manual control which has been redesigned after the embarrassing incident in Victory Circle at one of our test locations.

    When we first approached drivers to test our prototypes, the reaction was cautiously positive and even a bit skeptical. After using the product all but one drive was enthusiastic. The usual response was, "Where can I get me one of these ?"

    In this our first season, a certain few select drivers will be using our device in select races. For those of you interested from a scientific viewpoint I will be able to Email, at your request, car #s and races 5 days before each event. For those drivers who are constantly mobbed by hordes of beautiful women, the location of the manual button is being kept secret.

  • Not very practical... (Score:5, Insightful)

    by Goonie ( 8651 ) * <.robert.merkel. .at. .benambra.org.> on Monday June 21, 2004 @07:10AM (#9482961) Homepage
    I've seen this story floating round, and colour me unimpressed.

    Genetic algorithms are terribly clever, and are useful for many purposes, but to make them work you need a "fitness function" - the ability to check how good a solution is. And, seeing you're going to need to apply it to every member of the population in each generation, it better be pretty bloody low-overhead, and be a pretty close approximation of the real-world fitness of a solution. In fact, in my admittedly limited experience with them I found that 99.9% of the difficulty in applying genetic algorithms to a problem is finding an appropriate fitness function.

    The fitness function these guys have used is to use a racing simulation game and run the race electronically. That's good if you're trying to set up a car to win that game, but if you're actually trying to win a real car race with a real car, if the only fitness function you have is sending your driver out for a few million trial laps it's just not going to cut it.

    If, on the other hand, they had built software that allowed them to specify the car settings and tell them what lap time the car would achieve, that would be really impressive, and then you could bolt on the GA optmizer to find the killer setup. But using GA's like they have done is just a party trick - cute, but not that impressive.

    • I agree they aren't going to get very close to a perfect fitness function.

      But what this kind of technique could be really great for is in-race optimisation. Can't decide whether the come in for a pit stop this lap or the next? Let the GA run a few hundred thousand simulations of the possible ways the race will progress and get a probablity weighted average of the payoffs from each strategy, taking into account current race position, likely pit-stop times, track condition etc.

    • Re:Not very practical... (Score:4, Informative)

      by pclminion ( 145572 ) on Monday June 21, 2004 @10:23AM (#9484595)
      That's good if you're trying to set up a car to win that game, but if you're actually trying to win a real car race with a real car, if the only fitness function you have is sending your driver out for a few million trial laps it's just not going to cut it.

      That's why for problems with very expensive fitness functions, it's often better to use a simulated annealing technique. In SA, there is only one individual, not a whole population, so you only have to evaluate fitness once per iteration instead of potentially hundreds or thousands of times.

      Simulated annealing works like this: make a random (or in some implementations, a heuristically guided) change to the current individual. Evaluate the new fitness. If the change has improved the fitness, accept the change. Otherwise, choose at random whether to accept the change, with the chance of acceptance slowly decreasing over time. Hence the term "simulated annealing," named after the process of annealing steel by cooling it slowly, which allows the crystal domains to enlarge.

      This means that sometimes changes are accepted which actually decrease the fitness, with the hope that you might perhaps be able to escape a local maximum on the fitness landscape.

      In my experience, simulated annealing often works well in the same situations that a GA works well. And it's much easier to implement, too.

    • Absolutely true.

      And different drivers will handle different car configurations differently. Give a driver inexperienced with a rear-weight-biased car a rear-weight-biased car, and after the first hairpin, he's going to be flying off the course backwards, and wondering why.

      Give a driver inexperienced with a front-weight-biased car, a front-weight-biased car, and he'll wonder why you put a front-weight-biased car on a racetrack.

  • It's a neat idea, but I can see a few problems. (Score:5, Interesting)

    by foxtrot ( 14140 ) on Monday June 21, 2004 @07:20AM (#9483005)
    Foremost from my amateur racer point of view is the cost: Being able to tune any one of 60 some-odd parameters probably means being able to swap out any one of 60 some-odd parts with some other part, so you've got to have one of every possible part on hand or be able to fabricate it.

    For an F1 team, cost's not so much a consideration, though, the trouble is time. To be able to change that many parameters means having someone get under the car, swap a pile of parts, and send the test driver back out on track to collect the info for the next evolution. Computer simulations are neat, but they're not perfect, and when you're talking about shaving fractions of a second, that small imperfection can throw it completely away.

    I also wonder if this would actually be useful in the real world with real conditions. The sun going behind a cloud for a while has a measurable effect on lap times. The amount of gas in the tank, the temperature of the track, all those things change the way a car handles on the edge. Often, race setup is to dial in a car to be a little tighter or looser than what you really wanted because you expect the track to come to you.

    And then there's a possibly even bigger problem: If you go out and look at two cars that are running identical lap times, chances are they're nothing even close to identically set up, because drivers aren't machines. One driver will like a certain setup, and another won't be able to do anything with it.

    • Re:It's a neat idea, but I can see a few problems. (Score:4, Informative)

      by kfg ( 145172 ) on Monday June 21, 2004 @08:39AM (#9483550)
      Foremost from my amateur racer point of view is the cost: Being able to tune any one of 60 some-odd parameters probably means being able to swap out any one of 60 some-odd parts with some other part, so you've got to have one of every possible part on hand or be able to fabricate it.

      Well, no, not exactly. Do you use adjustable dampers on your car? Simple bump/rebound adjustment is 8 parameters (each wheel is a seperate system) right there alone. Roll bar lever arm length adustment, another two. Tire pressure, another four. Camber, another four. Toe, another four.

      We're up to 22 so far and haven't spent a penny or changed a part, nor have we yet exhausted simple suspension settings. Toe, 26. Castor, 28. Anti dive/squat, 30. Half way there already.

      Front and rear wing angles, brake bias, weight distribution. More stuff that simple adjustable.

      Ok, let's look at some of the parts that are commonly changed. Tires. Did you think of tires as a part? They are. They're a parameter. How many compounds have you got, hard/soft/wet? Maybe you're poor and only have three sets of springs, hard/medium/soft

      We're over our 60 parameters now and are still well within the range of changes that an amatuer racer would consider common and haven't touched the gearbox yet.

      Which is why we are also still within the range of simple car adjustments allowed in a video game which doesn't allow for fabrication of unique parts.

      Assuming you race in a catagory that allows these changes. Many amatuer, and even "entry level" pro catagories deal with the issue by simply disallowing changes. If you race Formula Vee/Star Mazda/Spec Miata/Barber Dodge you aren't going to be doing anything like changing suspension arms.

      60 parameters is nothin'.

      KFG

  • Already done in practice... (Score:4, Insightful)

    by Telcontar ( 819 ) on Monday June 21, 2004 @08:42AM (#9483590) Homepage
    This is already done in practice to optimize individual *parts* of a car. Certain desired/required parameters are given (dimensions as far as prescribed by regulations, necessary stiffness to survive race distance etc.). Others are variable (detailed geometry of individual parts). However, 70 parameters are just enough to model a single part, such as the shape of the nose.

    The insight of the design at large still has to come from an engineer. Genetic algorithms are then used to fine-tune that design. Applying the algorithm is still hard because it requires a lot of knowledge of the physics involved. Once you have this, you can be quite successful because everyone is craving to optimize a few percent.

  • And how does this compare with other methods? (Score:3, Informative)

    by exp(pi*sqrt(163)) ( 613870 ) on Monday June 21, 2004 @11:45AM (#9485607) Journal
    I expect I could do a lot better with traditional optimization methods. Genetic algorithms are notoriously slow at converging and are only any good when all other methods fail. I expect that for a racing simulation the output is, almost everywhere, a differentiable function of the input parameters, and hence you can use some kind of calculus based minimization algorithm. People use adjoint methods all the time to differentiate fluid dynamics simulations or orbital manoeuvers so I don't see that these methods would fail for a racing sim. In fact this [yorku.ca] paper is probably a good place to start.

Slashdot Top Deals

GREAT MOMENTS IN HISTORY (#7): April 2, 1751 Issac Newton becomes discouraged when he falls up a flight of stairs.

Close