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gaoptimset
Create a genetic algorithm options structure
Syntax
options = gaoptimset
gaoptimset
options = gaoptimset('param1',value1,'param2',value2,...)
options = gaoptimset(oldopts,'param1',value1,...)
options = gaoptimset(oldopts,newopts)
Description
options = gaoptimset (with no input arguments) creates a structure called options that contains the options, or parameters, for the genetic algorithm and sets parameters to their default values.
gaoptimset with no input or output arguments displays a complete list of parameters with their valid values.
options = gaoptimset('param1',value1,'param2',value2,...) creates a structure options and sets the value of 'param1' to value1, 'param2' to value2, and so on. Any unspecified parameters are set to their default values. It is sufficient to type only enough leading characters to define the parameter name uniquely. Case is ignored for parameter names.
options = gaoptimset(oldopts,'param1',value1,...) creates a copy of oldopts, modifying the specified parameters with the specified values.
options = gaoptimset(oldopts,newopts) combines an existing options structure, oldopts, with a new options structure, newopts. Any parameters in newopts with nonempty values overwrite the corresponding old parameters in oldopts.
Options
The following table lists the options you can set with gaoptimset. See Genetic Algorithm Options for a complete description of these options and their values. Values in {} denote the default value. You can also view the optimization parameters and defaults by typing gaoptimset at the command line.
Option
|
Description
|
Values
|
|---|
CreationFcn
|
Handle to the function that creates the initial population
|
{@gacreationuniform}
|
CrossoverFraction
|
The fraction of the population at the next generation, not including elite children, that is created by the crossover function
|
Positive scalar | {0.8}
|
CrossoverFcn
|
Handle to the function that the algorithm uses to create crossover children
|
@crossoverheuristic
{@crossoverscattered}
@crossoverintermediate
@crossoversinglepoint
@crossovertwopoint
|
EliteCount
|
Positive integer specifying how many individuals in the current generation are guaranteed to survive to the next generation
|
Positive integer | {2}
|
FitnessLimit
|
Scalar. If the fitness function attains the value of FitnessLimit, the algorithm halts.
|
Scalar | {-Inf}
|
FitnessScalingFcn
|
Handle to the function that scales the values of the fitness function
|
@fitscalinggoldberg
{@fitscalingrank}
@fitscalingprop
@fitscalingtop
|
Generations
|
Positive integer specifying the maximum number of iterations before the algorithm halts
|
Positive integer |{100}
|
PopInitRange
|
Matrix or vector specifying the range of the individuals in the initial population
|
Matrix or vector | [0;1]
|
PopulationType
|
String describing the data type of the population
|
'bitstring' | 'custom' | {'doubleVector'}
|
HybridFcn
|
Handle to a function that continues the optimization after ga terminates
|
Function handle | {[]}
|
InitialPopulation
|
Initial population
|
Positive scalar | {[]}
|
InitialScores
|
Initial scores
|
Column vector | {[]}
|
MigrationDirection
|
Direction of migration
|
'both' | {'forward'}
|
MigrationFraction
|
Scalar between 0 and 1 specifying the fraction of individuals in each subpopulation that migrates to a different subpopulation
|
Scalar | {0.2}
|
MigrationInterval
|
Positive integer specifying the number of generations that take place between migrations of individuals between subpopulations
|
Positive integer | {20}
|
MutationFcn
|
Handle to the function that produces mutation children
|
@mutationuniform
{@mutationgaussian}
|
OutputFcns
|
Array of handles to functions that ga calls at each iteration.
|
Array | {[]}
|
OutputInterval
|
Positive integer specifying the number of generations between consecutive calls to the output functions
|
Positive integer | {1}
|
PlotFcns
|
Array of handles to functions that plot data computed by the algorithm
|
@gaplotbestf @gaplotbestgenome @gaplotdistance @gaplotexpectation @gaplotgeneology @gaplotselection @gaplotrange @gaplotscorediversity @gaplotscores @gaplotstopping | {[]}
|
PlotInterval
|
Positive integer specifying the number of generations between consecutive calls to the plot functions
|
Positive integer | {1}
|
PopulationSize
|
Size of the population
|
Positive integer | {20}
|
SelectionFcn
|
Handle to the function that selects parents of crossover and mutation children
|
@selectiongoldberg
@selectionrandom
{@selectionstochunif}
@selectionroulette
@selectiontournament
|
StallLimitG
|
Positive integer. The algorithm stops if there is no improvement in the objective function for StallLimitG consecutive generations.
|
Positive integer | {50}
|
StallLimitS
|
Positive scalar. The algorithm stops if there is no improvement in the objective function for StallLimitS seconds.
|
Positive scalar | {20}
|
TimeLimit
|
Positive scalar. The algorithm stops after running for TimeLimit seconds.
|
Positive scalar | {30}
|
Vectorized
|
String specifying whether the computation of the fitness function is vectorized
|
'on' | {'off'}
|
See Also
gaoptimget, gatool
| | gaoptimget | | gatool | |