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From Thomas Neidhart <>
Subject Re: [Math] "FixedElapsedTime" in genetic algorithm (Was: svn commit: r1409352 - ...)
Date Thu, 15 Nov 2012 08:55:37 GMT
On 11/15/2012 09:08 AM, Thomas Neidhart wrote:
> On 11/15/2012 02:23 AM, Gilles Sadowski wrote:
>> On Wed, Nov 14, 2012 at 04:38:47PM -0800, Phil Steitz wrote:
>>> On 11/14/12 4:05 PM, Gilles Sadowski wrote:
>>>> Hi.
>>>> On Wed, Nov 14, 2012 at 08:09:52PM -0000, wrote:
>>>>> Author: tn
>>>>> Date: Wed Nov 14 20:09:51 2012
>>>>> New Revision: 1409352
>>>>> URL:
>>>>> Log:
>>>>> Reduce sleep time in unit test to prevent possible race condition.
>>>>> Modified:
>>>>>     commons/proper/math/trunk/src/test/java/org/apache/commons/math3/genetics/
>>>>> Modified: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/genetics/
>>>>> URL:
>>>>> ==============================================================================
>>>>> --- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/genetics/
>>>>> +++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/genetics/
Wed Nov 14 20:09:51 2012
>>>>> @@ -59,7 +59,7 @@ public class FixedElapsedTimeTest {
>>>>>          while (!tec.isSatisfied(pop)) {
>>>>>              try {
>>>>> -                Thread.sleep(500);
>>>>> +                Thread.sleep(50);
>>>>>              } catch (InterruptedException e) {
>>>>>                  // ignore
>>>>>              }
>>>> I have never read this part of CM yet. But just this makes me think that
>>>> "FixedElapsedTime" class should not exist in CM. Indeed, using it makes the
>>>> algorithm behave differently at each run!
>>>> I don't deny the usefulness of allotting some amount of time for a
>>>> computation, but this has absolutely nothing to do with genetic algorithms
>>>> (nor any others).
>>>> CM can help in implementing this high-level requirement, by allowing
>>>> <some search algorithm> to be restarted with the result of a previous
>>>> but IMHO it is a bad idea to have the various CM codes directly implement
>>>> that functionality.
>>> I disagree.  This looks like a useful feature to me, contributed by
>>> a user.  GA algorithms are by nature iterative with run-time
>>> evaluated StoppingConditions.  FixedElapsedTime is a reasonable
>>> stopping condition supported by other GA frameworks.  The feature is
>>> well-documented, so users know exactly what it does.  In particular,
>>> the number of generations is not fixed, so using this
>>> StoppingCondition does not guarantee the same results on successive
>>> runs with the same input parameters.  GAs are often
>>> non-deterministic for other reasons (randomness in mutation), so
>>> this is not a practical issue for those choosing to use the feature.
>> This one is really not worth a fight, but something which you have to adapt
>> depending on the machine on which you run your program can only be qualified
>> as a toy feature (IMHO): Unless we talk about real-time processing (which GA
>> are not really suited for...), some fixed amount of time can be too small
>> (solution not found on a "slow" machine) or too large (solution found long
>> before the end of the alloted time slot, but the algo keeps the CPU busy).
>> As for "non-deterministic", we had several discussions on this ML about
>> being able to control the pseudo-random behaviour of the algorithms, by
>> using a fixed seed. This is the same here, should you use the same seed,
>> the search should go through the same steps. But in this case, it is
>> _impossible_ to reproduce the same result since the number of generations
>> will depend on how busy the machine is with other processes.
> tbh I do not see the issue here.
> As Phil pointed out, this is a commonly used stopping condition for GAs
> so it makes sense to have it supported by default.
> I do not see the link to a discussion about deterministic behavior of
> algorithms. It is much more similar to a fixed iteration count (a
> stopping condition that already exists), whereas in this case it is
> time-based (if a user prefers it this way).

I just re-read my post and wanted to clarify:

 * the algorithm is deterministic
 * the result of course not, as it depends on the number of generations

So you are right, getting the same result with such a stopping condition
is indeed tricky, but sometimes this is not the goal. You just want to
get the best solution so far, even if it is not the best possible.


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