octave-ga 0.10.0-1 / usr / share / octave / packages / ga-0.10.0 / doc-cache

# Created by Octave 3.6.1, Thu May 03 16:34:48 2012 UTC <root@zirconium>
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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.



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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
assert (steps(1, index_steps) < max_step_size); ## DEBUG



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 fix an entry of the steps (or parents) vector
assert (steps(1, index_steps) < m



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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.