ga solver en matlab

8/3/2019 GA Solver en Matlab

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GA Solver en Matlab



GA Solver

X = GA(FITNESSFCN,NVARS) finds the minimum of FITNESSFCN using

GA. NVARS is the dimension (number of design variables) of the

FITNESSFCN. FITNESSFCN accepts a vector X of size 1-by-NAVRS,

and returns a scalar evaluated at X.

X = GA(FITNESSFCN,NAVRS,OPTIONS) finds the minimum for

FITNESSFCN with the default optimization parameters replaced by values

in the structure OPTIONS. OPTIONS can be created with the GAOPTIMSET

function.

X = GA(PROBLEM) finds the minimum for PROBLEM. PROBLEM is a structure

that has the following fields:

fitnessfcn: <Fitness Function>

nvars: <Number of design variables>

options: <Options structure created with GAOPTIMSET>

randstate: <Optional field to reset rand state>

randnstate: <Optional field to reset randn state>



GA Solver

[X, FVAL] = GA(FITNESSFCN, ...) returns FVAL, the value of the fitness

function FITNESSFCN at the solution X.

[X,FVAL,REASON] = GA(FITNESSFCN, ...) returns the REASON for stopping.

[X,FVAL,REASON,OUTPUT] = GA(FITNESSFCN, ...) returns a

structure OUTPUT with the following information:randstate: <State of the function RAND used before GA started>

randnstate: <State of the function RANDN used before GA started>

generations: <Total generations, excluding HybridFcn iterations>

funccount: <Total function evaluations>

message: <GA termination message>

[X,FVAL,REASON,OUTPUT,POPULATION] = GA(FITNESSFCN, ...) returns the final

POPULATION at termination.

[X,FVAL,REASON,OUTPUT,POPULATION,SCORES] = GA(FITNESSFCN, ...) returns the

SCORES of the final POPULATION.



GA Solver

There are several steps to the GA:

population generation

scoring

loop

fitness

scalingselection

crossover

mutation

scoring

migration

outputtermination testing

end loop

Each of these steps can be controlled by the options structure created

by GAOPTIMSET.



GA Solver

Example:

Minimize 'rastriginsfcn' fitness function of numberOfVariables = 2

x = ga(@rastriginsfcn,2)Display plotting functions while GA minimizes

options = gaoptimset('PlotFcns',...

{@gaplotbestf,@gaplotbestindiv,@gaplotexpectation,@gaplotstopping});

[x,fval,reason,output] =ga(@rastriginsfcn,2,options)



Opciones del algoritmo I

GAOPTIMSET Create a genetic algorithm options structure.

GAOPTIMSET returns a listing of the fields in the options structure aswell as valid parameters and the default parameter.

OPTIONS = GAOPTIMSET('PARAM',VALUE) creates a structure with the

default parameters used for all PARAM not specified, and will use thepassed argument VALUE for the specified PARAM.

OPTIONS = GAOPTIMSET('PARAM1',VALUE1,'PARAM2',VALUE2,....) will create astructure with the default parameters used for all fields not specified.Those FIELDS specified will be assigned the corresponding VALUE passed,PARAM and VALUE should be passed as pairs.

OPTIONS = GAOPTIMSET(OLDOPTS,'PARAM',VALUE) will create a structure namedOPTIONS. OPTIONS is created by altering the PARAM specified of OLDOPTS to

become the VALUE passed.

OPTIONS = GAOPTIMSET(OLDOPTS,'PARAM1',VALUE1,'PARAM2',VALUE2,...) willreassign those fields in OLDOPTS specified by PARAM1, PARAM2, ... toVALUE1, VALUE2, ...



Opciones del algoritmo IPopulationType - The type of Population being entered

[ 'bitstring' | 'custom' | {'doubleVector'} ]

PopInitRange - Initial range of values a population may have

[ Matrix | {[0;1]} ]

PopulationSize - Positive scalar indicating the number of individuals

[ positive scalar | {20} ]

EliteCount - Number of best individuals that survive to next

generation without any change[ positive scalar | {2} ]

CrossoverFraction - The fraction of genes swapped between individuals

[ positive scalar | {0.8} ]

MigrationDirection - Direction that fittest individuals from the various

sub-populations may migrate to other sub-populations

['both' | {'forward'}]

MigrationInterval - The number of generations between the migration of the fittest individuals to other sub-populations


MigrationFraction - Fraction of those individuals scoring the best

that will migrate

[ positive scalar | {0.2} ]

Generations - Number of generations to be simulated




Opciones del algoritmo ITimeLimit - The total time (in seconds) allowed for simulation

[ positive scalar | {INF} ]FitnessLimit - The lowest allowed score

[ scalar | {-Inf} ]

StallGenLimit - If after this number of generations there is

no improvement, the simulation will end


StallTimeLimit - If after this many seconds there is no improvement,

the simulation will end


InitialPopulation - The initial population used in seeding the GA

algorithm

[ Matrix | {[]} ]

InitialScores - The initial scores used to determine fitness; used

in seeding the GA algorithm

[ column vector | {[]} ]


CreationFcn - Function used to generate initial population

[ {@gacreationuniform} ]

FitnessScalingFcn - Function used to scale fitness scores.

[ @fitscalingshiftlinear | @fitscalingprop | @fitscalingtop |

{@fitscalingrank} ]



Opciones del algoritmo ISelectionFcn - Function used in selecting parents for next generation

[ @selectionremainder | @selectionrandom |@selectionroulette | @selectiontournament |{@selectionstochunif} ]

CrossoverFcn - Function used to do crossover [ @crossoverheuristic | @crossoverintermediate |

@crossoversinglepoint | @crossovertwopoint |

{@crossoverscattered} ]MutationFcn - Function used in mutating genes

[ @mutationuniform | {@mutationgaussian} ]

HybridFcn - Another optimization function to be used once GAhas normally terminated (for whatever reason)

[ @fminsearch | @patternsearch | @fminunc | {[]} ]Display - Level of display

[ 'off' | 'iter' | 'diagnose' | {'final'} ]OutputFcns - Function(s) called in every generation. This is more

general than PlotFcns.

[ @gaoutputgen | {[]} ]PlotFcns - Function(s) used in plotting various quantities

during simulation[ @gaplotbestf | @gaplotbestindiv | @gaplotdistance |@gaplotexpectation | @gaplotgeneology | @gaplotselection |@gaplotrange | @gaplotscorediversity | @gaplotscores |@gaplotstopping | {[]} ]

PlotInterval - The number of generations between plotting results[ positive scalar | {1} ]

Vectorized - Objective function is vectorized and it can evaluate

more than one point in one call[ 'on' | {'off'} ]

ga solver en matlab

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