[SciPy-User] Attached multiprocessing parallel Differential Evolution GA code

[James Phillips]

Below and attached is my first cut at a Python multiprocessing module parallel implementation of the Differential Evolution genetic algorithm.  The code is explicitly in the public domain.

While this does run in parallel processes, I do not see 100% CPU utilization for the child processes on my 4-core test server.  I suspect this is due to locking on the multiprocessing.Array objects, where the entire array is being locked at each access rather than only the requested elements in the array.

Note the "coefficient polishing on" option's use of scipy.optimize.fmin at each new "best solution" during iteration of the genetic algorithm, this vastly speeds up the GA runs.

I did not cross-post to the numpy discussion list, not sure if I should.

     James Phillips
     zunzun AT zunzun.com
     http://zunzun.com


---------------------------------------

#    testParallelDESolver.py
#
#    Placed into the public domain 1 August, 2009
#    James R. Phillips
#    2548 Vera Cruz Drive
#    Birmingham, AL 35235 USA
#    email: zun...@zunzun.com
#    http://zunzun.com

import ParallelDESolver, numpy, time

class TestSolver(ParallelDESolver.ParallelDESolver):

    #some test data
    xData = numpy.array([5.357, 9.861, 5.457, 5.936, 6.161, 6.731])
    yData = numpy.array([0.376, 7.104, 0.489, 1.049, 1.327, 2.077])


    def externalEnergyFunction(self, trial):
        # inverse exponential with offset, y = a * exp(b/x) + c
        predicted = trial[0] * numpy.exp(trial[1] / self.xData) + trial[2]

        # sum of squared error
        error = predicted - self.yData
        return numpy.sum(error*error)



if __name__ == '__main__':

    print
    print '  Running test generating new random numbers and coefficient polisher off, estimated worst case time'
    solver = TestSolver(4, 600, 600, -10, 10, "Rand2Exp", 0.7, 0.6, 0.01, False, False)
    tStart = time.time()
    atSolution, generations, bestEnergy, bestSolution = solver.Solve()
    print ' ', time.time() - tStart, 'seconds elapsed.  atSolution =', atSolution, ':', generations, 'generations passed, bestEnergy =', bestEnergy
    print '  coefficients:',
    for i in bestSolution:
        print i,
    print
    print

    print '  Running test generating new random numbers and coefficient polisher on, estimated medium case time'
    solver = TestSolver(4, 600, 600, -10, 10, "Rand2Exp", 0.7, 0.6, 0.01, False, True)
    tStart = time.time()
    atSolution, generations, bestEnergy, bestSolution = solver.Solve()
    print ' ', time.time() - tStart, 'seconds elapsed.  atSolution =', atSolution, ':', generations, 'generations passed, bestEnergy =', bestEnergy
    print '  coefficients:',
    for i in bestSolution:
        print i,
    print
    print

    print '  Running test recycling random numbers and coefficient polisher on, estimated best case time'
    solver = TestSolver(4, 600, 600, -10, 10, "Rand2Exp", 0.7, 0.6, 0.01, True, True)
    tStart = time.time()
    atSolution, generations, bestEnergy, bestSolution = solver.Solve()
    print ' ', time.time() - tStart, 'seconds elapsed.  atSolution =', atSolution, ':', generations, 'generations passed, bestEnergy =', bestEnergy
    print '  coefficients:',
    for i in bestSolution:
        print i,
    print
    print


-------------------------------------------


#    ParallelDESolver.py
#
#    Placed into the public domain 1 August, 2009
#    James R. Phillips
#    2548 Vera Cruz Drive
#    Birmingham, AL 35235 USA
#    email: zun...@zunzun.com
#    http://zunzun.com


import sys, os, numpy, random, scipy.optimize, multiprocessing


def parallelProcessPoolInitializer(in_sharedMemoryPopulation, in_sharedMemoryPopulationEnergies, in_sharedMemoryBestEnergy, in_sharedMemoryBestSolution, in_sharedMemoryAtSolution, in_sharedMemoryGeneration, in_solver):
    global sharedMemoryPopulation
    global sharedMemoryPopulationEnergies
    global sharedMemoryBestEnergy
    global sharedMemoryBestSolution
    global sharedMemoryAtSolution
    global sharedMemoryGeneration
    global solver

    sharedMemoryPopulation = in_sharedMemoryPopulation
    sharedMemoryPopulationEnergies = in_sharedMemoryPopulationEnergies
    sharedMemoryBestEnergy = in_sharedMemoryBestEnergy
    sharedMemoryBestSolution = in_sharedMemoryBestSolution
    sharedMemoryAtSolution = in_sharedMemoryAtSolution
    sharedMemoryGeneration = in_sharedMemoryGeneration
    solver = in_solver


def parallelProcessFunction(candidate):
    global sharedMemoryPopulation
    global sharedMemoryPopulationEnergies
    global sharedMemoryBestEnergy
    global sharedMemoryBestSolution
    global sharedMemoryAtSolution
    global sharedMemoryGeneration
    global solver

    if sharedMemoryAtSolution.value != 0: # has some other process found a solution?
        return

    eval('solver.' + solver.deStrategy + '(candidate)')
    trialEnergy, atSolution = solver.EnergyFunction(solver.trialSolution)
   
    if solver.polishTheBestTrials == True and trialEnergy < sharedMemoryBestEnergy.value and sharedMemoryGeneration.value > 0: # not the first generation
        # try to polish these new coefficients a bit.
        solver.trialSolution = scipy.optimize.fmin(solver.externalEnergyFunction, solver.trialSolution, disp = 0) # don't print warning messages to stdout
        trialEnergy, atSolution = solver.EnergyFunction(solver.trialSolution) # recalc with polished coefficients
   
    if trialEnergy < sharedMemoryPopulationEnergies[candidate]: # setting two shared memory objects, need to prevent contention here
        sharedMemoryPopulationEnergies[candidate] = trialEnergy
        for i in range(len(solver.trialSolution)):
            sharedMemoryPopulation[candidate * solver.parameterCount + i] = solver.trialSolution[i]

        # If at an all-time low, save to "best"
        if trialEnergy < sharedMemoryBestEnergy.value: # setting two shared memory objects, need to prevent contention here
            #print 'New best energy in generation', sharedMemoryGeneration.value, 'os.getpid() =', os.getpid(), 'new best energy', trialEnergy
            sharedMemoryBestEnergy.value = trialEnergy
            for i in range(len(solver.trialSolution)):
                sharedMemoryBestSolution[i] = solver.trialSolution[i]
           
    # if we've reached a sufficient solution, let the other processes know
    if atSolution == True:
        #print os.getpid(), 'At solution.'
        sharedMemoryAtSolution.value = 1


class ParallelDESolver:

    def __init__(self, parameterCount, populationSize, maxGenerations, minInitialValue, maxInitialValue, deStrategy, diffScale, crossoverProb, cutoffEnergy, useClassRandomNumberMethods, polishTheBestTrials):

        self.polishTheBestTrials = polishTheBestTrials # see the Solve method where this flag is used
        self.maxGenerations      = maxGenerations
        self.parameterCount      = parameterCount
        self.populationSize      = populationSize
        self.cutoffEnergy        = cutoffEnergy
        self.minInitialValue     = minInitialValue
        self.maxInitialValue     = maxInitialValue
        self.deStrategy          = deStrategy # deStrategy is the name of the DE function to use
        self.useClassRandomNumberMethods = useClassRandomNumberMethods

        self.scale = diffScale
        self.crossOverProbability = crossoverProb

        self.generation = 1 # for testing, actual value set on the Solve() method

        if True == self.useClassRandomNumberMethods:
            self.SetupClassRandomNumberMethods(3)
       
       
    def Solve(self):

        # a random initial population in shared memory
        sharedMemoryPopulation = multiprocessing.Array('d', [0.0] * (self.populationSize * self.parameterCount))
        for i in range(self.populationSize * self.parameterCount):
            sharedMemoryPopulation[i] = random.uniform(self.minInitialValue, self.maxInitialValue)

        # initial population assumed to have high energies
        sharedMemoryPopulationEnergies = multiprocessing.Array('d', [1.0E300] * self.populationSize)
       
        # a shared memory "best energy" for internal comparisons
        sharedMemoryBestEnergy = multiprocessing.Value('d', 1.0E300)
       
        # best solution, will be copied into by worker processes
        sharedMemoryBestSolution = multiprocessing.Array('d', [0.0] * self.parameterCount)
       
        # if solution has been reached, we are done
        sharedMemoryAtSolution = multiprocessing.Value('i', 0)

        # allows check for "generation 0"
        sharedMemoryGeneration = multiprocessing.Value('i', 0)


        initialArguments = [sharedMemoryPopulation, sharedMemoryPopulationEnergies, sharedMemoryBestEnergy, sharedMemoryBestSolution, sharedMemoryAtSolution, sharedMemoryGeneration, self]
        processPool = multiprocessing.Pool(initializer=parallelProcessPoolInitializer, initargs=initialArguments)
       
        for sharedMemoryGeneration.value in range(self.maxGenerations):
            #print 'new gen'
            if sharedMemoryAtSolution.value != 0:
                break
            processPool.map(parallelProcessFunction, range(self.populationSize))


        return sharedMemoryAtSolution.value, sharedMemoryGeneration.value, sharedMemoryBestEnergy.value, sharedMemoryBestSolution


    def SetupClassRandomNumberMethods(self, in_RandomSeed):
        #print 'Generating random numbers for recycling'
        numpy.random.seed(in_RandomSeed) # this yields same results each time Solve() is run
        self.nonStandardRandomCount = self.populationSize * self.parameterCount * 3 + 1 # the "+1" makes an odd number of array items
        if self.nonStandardRandomCount < 523: # set a minimum number of random numbers
            self.nonStandardRandomCount = 523
           
        self.ArrayOfRandomIntegersBetweenZeroAndParameterCount = numpy.random.random_integers(0, self.parameterCount-1, size=(self.nonStandardRandomCount))
        self.ArrayOfRandomRandomFloatBetweenZeroAndOne = numpy.random.uniform(size=(self.nonStandardRandomCount))
        self.ArrayOfRandomIntegersBetweenZeroAndPopulationSize = numpy.random.random_integers(0, self.populationSize-1, size=(self.nonStandardRandomCount))
        self.randCounter1 = 0
        self.randCounter2 = 0
        self.randCounter3 = 0


    def GetClassRandomIntegerBetweenZeroAndParameterCount(self):
        self.randCounter1 += 1
        if self.randCounter1 >= self.nonStandardRandomCount:
            self.randCounter1 = 0
        return self.ArrayOfRandomIntegersBetweenZeroAndParameterCount[self.randCounter1]

    def GetClassRandomFloatBetweenZeroAndOne(self):
        self.randCounter2 += 1
        if self.randCounter2 >= self.nonStandardRandomCount:
            self.randCounter2 = 0
        return self.ArrayOfRandomRandomFloatBetweenZeroAndOne[self.randCounter2]
       
    def GetClassRandomIntegerBetweenZeroAndPopulationSize(self):
        self.randCounter3 += 1
        if self.randCounter3 >= self.nonStandardRandomCount:
            self.randCounter3 = 0
        return self.ArrayOfRandomIntegersBetweenZeroAndPopulationSize[self.randCounter3]


    # this class might normally be subclassed and this method overridden, or the
    # externalEnergyFunction set and this method used directly
    def EnergyFunction(self, trial):
        try:
            energy = self.externalEnergyFunction(trial)
        except ArithmeticError:
            energy = 1.0E300 # high energies for arithmetic exceptions
        except FloatingPointError:
            energy = 1.0E300 # high energies for floating point exceptions

        # we will be "done" if the energy is less than or equal to the cutoff energy
        if energy <= self.cutoffEnergy:
            return energy, True
        else:
            return energy, False

    def Best1Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale * (sharedMemoryPopulation[r1 * self.parameterCount + n] - sharedMemoryPopulation[r2 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand1Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 * self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 * self.parameterCount + n] - sharedMemoryPopulation[r3 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def RandToBest1Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] += self.scale * (self.bestSolution[n] - self.trialSolution[n]) + self.scale * (sharedMemoryPopulation[r1 * self.parameterCount + n] - sharedMemoryPopulation[r2 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Best2Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale * (sharedMemoryPopulation[r1 * self.parameterCount + n] + sharedMemoryPopulation[r2 * self.parameterCount + n] - sharedMemoryPopulation[r3 * self.parameterCount + n] - sharedMemoryPopulation[r4 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand2Exp(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,1)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 * self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 * self.parameterCount + n] + sharedMemoryPopulation[r3 * self.parameterCount + n] - sharedMemoryPopulation[r4 * self.parameterCount + n] - sharedMemoryPopulation[r5 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Best1Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale * (sharedMemoryPopulation[r1 * self.parameterCount + n] - sharedMemoryPopulation[r2 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand1Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 * self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 * self.parameterCount + n] - sharedMemoryPopulation[r3 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def RandToBest1Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,0,0,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] += self.scale * (self.bestSolution[n] - self.trialSolution[n]) + self.scale * (sharedMemoryPopulation[r1 * self.parameterCount + n] - sharedMemoryPopulation[r2 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Best2Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,0)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = self.bestSolution[n] + self.scale * (sharedMemoryPopulation[r1 * self.parameterCount + n] + sharedMemoryPopulation[r2 * self.parameterCount + n] - sharedMemoryPopulation[r3 * self.parameterCount + n] - sharedMemoryPopulation[r4 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def Rand2Bin(self, candidate):
        r1,r2,r3,r4,r5 = self.SelectSamples(candidate, 1,1,1,1,1)
        if True == self.useClassRandomNumberMethods:
            n = self.GetClassRandomIntegerBetweenZeroAndParameterCount()
        else:
            n = random.randint(0, self.parameterCount-1)

        self.trialSolution = sharedMemoryPopulation[candidate * self.parameterCount  :  candidate * self.parameterCount + self.parameterCount]
        i = 0
        while(1):
            if True == self.useClassRandomNumberMethods:
                k = self.GetClassRandomFloatBetweenZeroAndOne()
            else:
                k = random.uniform(0.0, 1.0)
            if k >= self.crossOverProbability or i == self.parameterCount:
                break
            self.trialSolution[n] = sharedMemoryPopulation[r1 * self.parameterCount + n] + self.scale * (sharedMemoryPopulation[r2 * self.parameterCount + n] + sharedMemoryPopulation[r3 * self.parameterCount + n] - sharedMemoryPopulation[r4 * self.parameterCount + n] - sharedMemoryPopulation[r5 * self.parameterCount + n])
            n = (n + 1) % self.parameterCount
            i += 1


    def SelectSamples(self, candidate, r1, r2, r3, r4, r5):
        if r1:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r1 = self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r1 = random.randint(0, self.populationSize-1)
                if r1 != candidate:
                    break
        if r2:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r2 = self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r2 = random.randint(0, self.populationSize-1)
                if r2 != candidate and r2 != r1:
                    break
        if r3:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r3 = self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r3 = random.randint(0, self.populationSize-1)
                if r3 != candidate and r3 != r1 and r3 != r2:
                    break
        if r4:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r4 = self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r4 = random.randint(0, self.populationSize-1)
                if r4 != candidate and r4 != r1 and r4 != r2 and r4 != r3:
                    break
        if r5:
            while(1):
                if True == self.useClassRandomNumberMethods:
                    r5 = self.GetClassRandomIntegerBetweenZeroAndPopulationSize()
                else:
                    r5 = random.randint(0, self.populationSize-1)
                if r5 != candidate and r5 != r1 and r5 != r2 and r5 != r3 and r5 != r4:
                    break

        return r1, r2, r3, r4, r5

[Alan G Isaac]

On 8/1/2009 10:58 AM James Phillips apparently wrote:
> Below and attached is my first cut at a Python multiprocessing module
> parallel implementation of the Differential Evolution genetic
> algorithm. The code is explicitly in the public domain.

Thanks for posting this. I confess I was unaware of the
differential evolution approach to optimization.

Are you proposing adding this functionality to scipy.optimize?
Or what? And if so, how to proceed?

Alan Isaac

_______________________________________________
SciPy-User mailing list
SciPy...@scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user

[James Phillips]

Alan G Isaac <aisaac <at> american.edu> writes:
>
> Are you proposing adding this functionality to scipy.optimize?
> Or what? And if so, how to proceed?

I'm not sure where to go with it from here, which is why I "public domain"-ed it.

James

[Robert Kern]

On Mon, Aug 3, 2009 at 08:34, Alan G Isaac<ais...@american.edu> wrote:
> On 8/1/2009 10:58 AM James Phillips apparently wrote:
>> Below and attached is my first cut at a Python multiprocessing module
>> parallel implementation of the Differential Evolution genetic
>> algorithm.  The code is explicitly in the public domain.
>
>
> Thanks for posting this.  I confess I was unaware of the
> differential evolution approach to optimization.
>
> Are you proposing adding this functionality to scipy.optimize?
> Or what? And if so, how to proceed?

A multiprocessing version might not be ideal to integrate into
scipy.optimize by itself, but I do have a single-process version here:

http://svn.scipy.org/svn/scipy/branches/sandbox/scipy/sandbox/rkern/diffev.py

It's quite a bit more numpy-idiomatic than James' version, which
follows the C++ reference implementation of DE fairly closely.

--
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco

[James Phillips]

Robert Kern <robert.kern <at> gmail.com> writes:
>
> A multiprocessing version might not be ideal to integrate into
> scipy.optimize by itself, but I do have a single-process version here:
>
> http://svn.scipy.org/svn/scipy/branches/sandbox/scipy/sandbox/rkern/diffev.py
>
> It's quite a bit more numpy-idiomatic than James' version, which
> follows the C++ reference implementation of DE fairly closely.

May I try my hand at parallelizing your code? If so, under what license would
you like me to release the result?

James

[Robert Kern]

On Tue, Aug 4, 2009 at 14:10, James Phillips<zun...@zunzun.com> wrote:
> Robert Kern <robert.kern <at> gmail.com> writes:
>>
>> A multiprocessing version might not be ideal to integrate into
>> scipy.optimize by itself, but I do have a single-process version here:
>>
>> http://svn.scipy.org/svn/scipy/branches/sandbox/scipy/sandbox/rkern/diffev.py
>>
>> It's quite a bit more numpy-idiomatic than James' version, which
>> follows the C++ reference implementation of DE fairly closely.
>
> May I try my hand at parallelizing your code?

By all means.

> If so, under what license would
> you like me to release the result?

BSD, please.

--
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco