octave-ga 0.10.0-2 / usr / share / octave / packages / ga-0.10.0 / doc-cache

# Created by Octave 3.8.2, Mon Sep 22 16:09:32 2014 UTC <root@rama>
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crossoverscattered


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 simplified example (nvars == 4)
 p1 = [varA varB varC varD]
 p2 = [var1 var2 var3 var4]
 b = [1 1 0 1]
 child = [varA varB var3 varD]



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 simplified example (nvars == 4)
 p1 = [varA varB varC varD]
 p2 = [var1 var2 va



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fitscalingrank


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TODO
ranks ([7,2,2]) == [3.0,1.5,1.5]
is [3,1,2] (or [3,2,1]) useful? 



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TODO
ranks ([7,2,2]) == [3.0,1.5,1.5]
is [3,1,2] (or [3,2,1]) useful? 




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ga


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 -- Function File: X = ga (FITNESSFCN, NVARS)
 -- Function File: X = ga (FITNESSFCN, NVARS, A, B)
 -- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ)
 -- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ, LB, UB)
 -- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ, LB, UB,
          NONLCON)
 -- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ, LB, UB,
          NONLCON, OPTIONS)
 -- Function File: X = ga (PROBLEM)
 -- Function File: [X, FVAL] = ga (...)
 -- Function File: [X, FVAL, EXITFLAG] = ga (...)
 -- Function File: [X, FVAL, EXITFLAG, OUTPUT] = ga (...)
 -- Function File: [X, FVAL, EXITFLAG, OUTPUT, POPULATION] = ga (...)
 -- Function File: [X, FVAL, EXITFLAG, OUTPUT, POPULATION, SCORES] = ga
          (...)
     Find minimum of function using genetic algorithm.

     *Inputs*
     FITNESSFCN
          The objective function to minimize.  It accepts a vector X of
          size 1-by-NVARS, and returns a scalar evaluated at X.
     NVARS
          The dimension (number of design variables) of FITNESSFCN.
     OPTIONS
          The structure of the optimization parameters; can be created
          using the 'gaoptimset' function.  If not specified, 'ga'
          minimizes with the default optimization parameters.
     PROBLEM
          A structure containing the following fields:
             * 'fitnessfcn'
             * 'nvars'
             * 'Aineq'
             * 'Bineq'
             * 'Aeq'
             * 'Beq'
             * 'lb'
             * 'ub'
             * 'nonlcon'
             * 'randstate'
             * 'randnstate'
             * 'solver'
             * 'options'

     *Outputs*
     X
          The local unconstrained found minimum to the objective
          function, FITNESSFCN.
     FVAL
          The value of the fitness function at X.

     See also: gaoptimset.




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Find minimum of function using genetic algorithm.



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gacreationuniform


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 -- Function File: POPULATION = gacreationuniform (GENOMELENGTH,
          FITNESSFCN, OPTIONS)
     Create a random initial population with a uniform distribution.

     *Inputs*
     GENOMELENGTH
          The number of indipendent variables for the fitness function.
     FITNESSFCN
          The fitness function.
     OPTIONS
          The options structure.

     *Outputs*
     POPULATION
          The initial population for the genetic algorithm.

     See also: ga, gaoptimset.




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Create a random initial population with a uniform distribution.



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gaoptimset


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 -- Function File: OPTIONS = gaoptimset
 -- Function File: OPTIONS = gaoptimset ('PARAM1', VALUE1, 'PARAM2',
          VALUE2, ...)
     Create genetic algorithm options structure.

     *Inputs*
     PARAM
          Parameter to set.  Unspecified parameters are set to their
          default values; specifying no parameters is allowed.
     VALUE
          Value of PARAM.

     *Outputs*
     OPTIONS
          Structure containing the options, or parameters, for the
          genetic algorithm.

     *Options*
     'CreationFcn'
     'CrossoverFcn'
     'CrossoverFraction'
     'EliteCount'
     'FitnessLimit'
     'FitnessScalingFcn'
     'Generations'
     'InitialPopulation'
          Can be partial.
     'InitialScores'
          column vector | [] (default) .  Can be partial.
     'MutationFcn'
     'PopInitRange'
     'PopulationSize'
     'SelectionFcn'
     'TimeLimit'
     'UseParallel'
          "always" | "never" (default) .  Parallel evaluation of
          objective function.  TODO: parallel evaluation of nonlinear
          constraints
     'Vectorized'
          "on" | "off" (default) .  Vectorized evaluation of objective
          function.  TODO: vectorized evaluation of nonlinear
          constraints

     See also: ga.




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Create genetic algorithm options structure.



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mutationgaussian


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 start mutationgaussian logic



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 start mutationgaussian logic




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rastriginsfcn


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 -- Function File: Y = rastriginsfcn (X)
     Rastrigin's function.




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Rastrigin's function.



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selectionstochunif


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 fix an entry of the steps (or parents) vector



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 fix an entry of the steps (or parents) vector




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test_ga


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 -- Script File: test_ga
     Execute all available tests at once.




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Execute all available tests at once.