gaoptimset.m

遗传算法工具包

function options = gaoptimset(varargin)
%GAOPTIMSET Create a genetic algorithm options structure.
%
%   GAOPTIMSET returns a listing of the fields in the options structure as
%   well 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 the
%   passed argument VALUE for the specified PARAM.
%
%   OPTIONS = GAOPTIMSET('PARAM1',VALUE1,'PARAM2',VALUE2,....) will create a
%   structure 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 named 
%   OPTIONS.  OPTIONS is created by altering the PARAM specified of OLDOPTS to
%   become the VALUE passed.  
%
%   OPTIONS = GAOPTIMSET(OLDOPTS,'PARAM1',VALUE1,'PARAM2',VALUE2,...) will
%   reassign those fields in OLDOPTS specified by PARAM1, PARAM2, ... to 
%   VALUE1, VALUE2, ...
%
%GAOPTIMSET PARAMETERS
%   
%   PopulationType      - 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
%                       [ positive scalar | {20} ]
%   MigrationFraction   - Fraction of those individuals scoring the best
%                         that will migrate
%                       [ positive scalar | {0.2} ]
%   Generations         - Number of generations to be simulated
%                       [ positive scalar | {100} ]
%   TimeLimit           - 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
%                       [ positive scalar | {50} ]
%   StallTimeLimit      - If after this many seconds there is no improvement,
%                         the simulation will end
%                       [ positive scalar | {20} ]
%   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 | {[]} ]
%                       [ positive scalar | {1} ]
%   CreationFcn         - Function used to generate initial population
%                       [ {@gacreationuniform} ]
%   FitnessScalingFcn   - Function used to scale fitness scores.
%                       [ @fitscalingshiftlinear | @fitscalingprop | @fitscalingtop |
%                         {@fitscalingrank} ]
%   SelectionFcn        - 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 GA 
%                         has 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'} ]

%   Copyright 2004 The MathWorks, Inc. 
%   $Revision: 1.5.6.2 $  $Date: 2004/03/09 16:15:35 $

if (nargin == 0) && (nargout == 0)
    fprintf('          PopulationType: [ ''bitstring''      | ''custom''    | {''doubleVector''} ]\n');
    fprintf('            PopInitRange: [ matrix           | {[0;1]} ]\n');
    fprintf('          PopulationSize: [ positive scalar  | {20} ]\n');
    fprintf('              EliteCount: [ positive scalar  | {2} ]\n');
    fprintf('       CrossoverFraction: [ positive scalar  | {0.8} ]\n\n');
    
    fprintf('      MigrationDirection: [ ''both''           | {''forward''} ]\n');
    fprintf('       MigrationInterval: [ positive scalar  | {20} ]\n');
    fprintf('       MigrationFraction: [ positive scalar  | {0.2} ]\n\n');
    
    fprintf('             Generations: [ positive scalar  | {100} ]\n');
    fprintf('               TimeLimit: [ positive scalar  | {Inf} ]\n');
    fprintf('            FitnessLimit: [ scalar           | {-Inf} ]\n\n');
    fprintf('           StallGenLimit: [ positive scalar  | {50} ]\n');
    fprintf('          StallTimeLimit: [ positive scalar  | {20} ]\n');
    
    fprintf('       InitialPopulation: [ matrix           | {[]} ]\n');
    fprintf('           InitialScores: [ column vector    | {[]} ]\n\n');
    
    fprintf('             CreationFcn: [ function_handle  | {@gacreationuniform} ]\n');
    fprintf('       FitnessScalingFcn: [ function_handle  | @fitscalingshiftlinear  | @fitscalingprop  | \n');
    fprintf('                            @fitscalingtop   | {@fitscalingrank} ]\n');
    fprintf('            SelectionFcn: [ function_handle  | @selectionremainder    | @selectionrandom | \n');
    fprintf('                            @selectionroulette | @selectiontournament   | {@selectionstochunif} ]\n');
    fprintf('            CrossoverFcn: [ function_handle  | @crossoverheuristic  | @crossoverintermediate | \n'); 
    fprintf('                            @crossoversinglepoint | @crossovertwopoint | {@crossoverscattered} ]\n');
    fprintf('             MutationFcn: [ function_handle  | @mutationuniform | {@mutationgaussian} ]\n');
    fprintf('               HybridFcn: [ @fminsearch | @patternsearch | @fminunc | {[]} ]\n\n');
    
    fprintf('                 Display: [ off | iter | diagnose | {final} ]\n');
    fprintf('              OutputFcns: [ function_handle  | @gaoutputgen | {[]} ]\n');
    fprintf('                PlotFcns: [ function_handle  | @gaplotbestf | @gaplotbestindiv | @gaplotdistance | \n');
    fprintf('                            @gaplotexpectation | @gaplotgeneology | @gaplotselection | @gaplotrange | \n');
    fprintf('                            @gaplotscorediversity  | @gaplotscores | @gaplotstopping  | {[]} ]\n');
    fprintf('            PlotInterval: [ positive scalar  | {1} ]\n\n');
        
    fprintf('              Vectorized: [ ''on''  | {''off''} ]\n');
    return; 
end     

numberargs = nargin; 

%Return options with default values and return it when called with one output argument
options=struct('PopulationType', 'doubleVector', ...
               'PopInitRange', [0;1], ...
               'PopulationSize', 20, ...
               'EliteCount', 2, ...
               'CrossoverFraction', 0.8, ...
               'MigrationDirection','forward', ...
               'MigrationInterval',20, ...
               'MigrationFraction',0.2, ...
               'Generations', 100, ...
               'TimeLimit', inf, ...
               'FitnessLimit', -inf, ...
               'StallGenLimit', 50, ...
               'StallTimeLimit', 20, ...
               'InitialPopulation',[], ...
               'InitialScores', [], ...
               'PlotInterval',1, ...
               'CreationFcn',@gacreationuniform, ...
               'FitnessScalingFcn', @fitscalingrank, ...
               'SelectionFcn', @selectionstochunif, ...
               'CrossoverFcn',@crossoverscattered, ...
               'MutationFcn',@mutationgaussian, ...
               'HybridFcn',[], ...
               'Display', 'final', ...
               'PlotFcns', [], ...
               'OutputFcns', [], ...
               'Vectorized','off');