[Paradiseo-help] [Paradiseo-users] [EO] Help with parameter control in evolution

Johann Dréo johann at dreo.fr
Mer 24 Avr 11:47:26 CEST 2013


If the two algorithms are supposed to be the same ones, this is strange and
could indeed be a parameter problem. This is impossible to tell without
having a look at the codes.

If the algorithms are different, you may keep in mind that the genetic
algorithms in the EO tutorials are quite old and maybe not competitive with
the current state of the art.

This would be nice to have a repository with the best algorithms
implemented in paradadisEO, but the main authors are busy implementing
dedicated solvers. Thus, maybe a new contributor would ave more time to
spend on that? :-)

-- 
Johann

P.S. posted back to the new mailing-list.


On Fri, Apr 5, 2013 at 8:47 PM, Derrik Asher <derrikasher at gmail.com> wrote:

> Hello,
>
> My name is Derrik Asher and I am a graduate student at the University of
> California, Irvine working with Dr. Krichmar in the Cognitive Anteater
> Robotics Lab (CARL).
>
> I am trying to get EO to produce the same results as another basic genetic
> algorithm using a mu-plus lambda paradigm.
>
> I have about 100 parameters using direct encoding. I'm not sure why EO
> can't find an equivalent solution to my other algorithm in a simple test
> task. The task is to set all 100 parameters to zero. EO gets close, but
> never reaches the optimal solution. My other algorithm completes the task
> in about 70 iterations, EO minimizes the values very quickly (5-10
> iterations) but never gets any closer (after 10000 iterations).
>
> I suspect that I am not using the EO parameters that I think I am, e.g.
> crossover and mutation. My parameter file is listed below. Please let me
> know if I am excluding particular parameters, or setting them incorrectly.
>
> Thank you very much,
>
> Derrik
>
> P.S. the parameter file is pasted below
>
>
> EO Parameter File
>
> --------------------------------------------------------------------------------------------------------------------------------------------------------------------------
> ######                 General    ######
> --help=0                                 # -h : Prints this message
> --stopOnUnknownParam=1                   # Stop if unkown param entered
> --seed=123456789                        # -S : Random number seed
>
> ######    ES mutation             ######
> --Isotropic=0                              # -i : Isotropic self-adaptive
> mutation
> --Stdev=0                                  # -s : One self-adaptive stDev
> per variable
> --Correl=0                                 # -c : Use correlated mutations
>
> ######    Evolution Engine        ######
> --popSize=1000                              # -P : Population Size
> --selection=DetTour(2)                    # -S : Selection: DetTour(T),
> StochTour(t), Roulette, Ranking(p,e) or Sequential(ordered/unordered)
> --nbOffspring=100%                           # -O : Nb of offspring
> (percentage or absolute)
>
> ##Comma replacement is also generational replacement if you the number of
> offspring is the same as the number of parents.
> --replacement=Plus                       # -R : Replacement: Comma, Plus or
> EPTour(T), SSGAWorst, SSGADet(T), SSGAStoch(t)
> --weakElitism=1                          # -w : Old best parent replaces
> new worst offspring *if necessary*
>
> ######    Genotype Initialization    ######
> --vecSize=10                             # -n : The number of variables
> --initBounds=10[-1.0,1.0]                   # -B : Bounds for
> initialization (MUST be bounded)
> --sigmaInit=0.3%                        # -s : Initial value for Sigmas
> (with a '%' -> scaled by the range of each variable)
>
> ######    Output                  ######
> --useEval=1                              # Use nb of eval. as counter (vs
> nb of gen.)
> --useTime=1                              # Display time (s) every
> generation
> --printBestStat=0                        # Print Best/avg/stdev every gen.
> --printPop=0                             # Print sorted pop. every gen.
>
> ######    Output - Disk           ######
> --resDir=datafolder               # Directory to store DISK outputs
> --eraseDir=0                             # erase files in dirName if any
> --fileBestStat=1                         # Output bes/avg/std to file
>
> ######    Output - Graphical      ######
> --plotBestStat=0                         # Plot Best/avg Stat
> --plotHisto=0                            # Plot histogram of fitnesses
>
> ######    Persistence             ######
> # --Load=                                  # -L : A save file to restart
> from
> # --recomputeFitness=0                     # -r : Recompute the fitness
> after re-loading the pop.?
> # --saveFrequency=0                        # Save every F generation (0 =
> only final state, absent = never)
> # --saveTimeInterval=0                     # Save every T seconds (0 or
> absent = never)
> # --status=t-eoESAll.status                # Status file
>
> ######    Stopping criterion      ######
> --maxGen=5000                             # -G : Maximum number of
> generations () = none)
> --steadyGen=1000                          # -s : Number of generations with
> no improvement
> --minGen=0                               # -g : Minimum number of
> generations
> --maxEval=0                              # -E : Maximum number of
> evaluations (0 = none)
> --targetFitness=0.01                     # -T : Stop when fitness reaches
> --CtrlC=0                                # -C : Terminate current
> generation upon Ctrl C
>
> ######    Variation Operators     ######
> # --objectBounds=10[-inf,+inf]             # -B : Bounds for variables
> # --operator=SGA                           # -o : Description of the
> operator (SGA only now)
> --pCross=1                                # -C : Probability of Crossover
> --pMut=0.5                                 # -M : Probability of Mutation
> --hypercubeRate=1                         # -A : Relative rate for
> hypercube crossover
> --uxoverRate=0                            # -A : Relative rate for uniform
> crossover
> --segmentRate=0                           # -s : Relative rate for segment
> crossover
> #--crossType=standard                       # -C : Type of ES recombination
> (global or standard)
> #--crossObj=discrete                      # -O : Recombination of object
> variables (discrete, intermediate or none)
> #--crossStdev=intermediate                # -S : Recombination of mutation
> strategy parameters (intermediate, discrete or none)
> #--TauLoc=1                               # -l : Local Tau (before
> normalization)
>
> --------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> --
> Derrik E. Asher
> Candidate for the degree of Ph.D., Cognitive Neuroscience
> University of California, Irvine
> Cognitive Sciences Department
> Cognitive Anteater Robotics Laboratory (CARL)
> Laboratory of Visual Neuroscience (LVN)
> Center for Cognitive Neuroscience (CCNS)
> http://www.cogsci.uci.edu/~dasher/
>
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