Create genetic algorithm options structure
gaoptimset
options = gaoptimset
options = gaoptimset(@ga)
options = gaoptimset(@gamultiobj)
options = gaoptimset('param1',value1,'param2',value2,...)
options = gaoptimset(oldopts,'param1',value1,...)
options = gaoptimset(oldopts,newopts)
gaoptimset
with no input or output arguments
displays a complete list of parameters with their valid values.
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 []
, indicating default values
will be used.
options = gaoptimset(@ga)
creates a structure
called options
that contains the default options
for the genetic algorithm.
options = gaoptimset(@gamultiobj)
creates
a structure called options
that contains the default
options for gamultiobj
.
options = gaoptimset('param1',value1,'param2',value2,...)
creates
a structure called 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
.
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. {}*
means the default when there
are linear constraints, and for MutationFcn
also
when there are bounds. You can also view the optimization parameters
and defaults by typing gaoptimset
at the command
line. I* indicates that ga
ignores
or overwrites the option for mixed integer optimization problems.
optimoptions
hides the options listed in italics,
but gaoptimset
does not. See Options that optimoptions Hides.
Options for ga
, Integer ga
,
and gamultiobj
Option  Description  Values 

ConstraintTolerance  Determines the feasibility with respect to nonlinear
constraints. Also, For  Positive scalar  
 I* Handle to the function that creates the initial population. See Population Options. 

 I* Handle to the function that the algorithm uses to create crossover children. See Crossover Options. 

 The fraction of the population at the next generation, not including elite children, that is created by the crossover function.  Positive scalar  
 Level of display. 

 Handle to the function that computes distance measure
of individuals. The value applies to decision variable or design space
(genotype) or to function space (phenotype). For 

 NM Positive integer
specifying how many individuals in the current generation are guaranteed
to survive to the next generation. Not used in  Positive integer  
 NM If the fitness function
attains the value of  Scalar  
 Handle to the function that scales the values of the
fitness function. Option unavailable for 

FunctionTolerance  The algorithm stops if the average relative change in
the best fitness function value over For  Positive scalar  
 I* Handle to a function
that continues the optimization after Alternatively, a cell array specifying the hybrid function and its options structure. See ga Hybrid Function. For  Function handle  or 1by2
cell array  
InitialPenalty  NM I* Initial value of penalty parameter  Positive scalar  
 Initial population used to seed the genetic algorithm.
Has up to For  Matrix  
 Matrix or vector specifying the range of the individuals
in the initial population. Applies to For  Matrix or vector  
 I* Initial scores used
to determine fitness. Has up to For  Column vector for single objective  matrix for multiobjective
 
 Maximum number of iterations before the algorithm halts. For  Positive integer  
 The algorithm stops if the average relative change in
the best fitness function value over For  Positive integer  
 NM The algorithm stops
if there is no improvement in the objective function for For  Positive scalar 
 The algorithm stops after running after For  Positive scalar  
MigrationDirection  Direction of migration. See Migration Options 

MigrationFraction  Scalar between 0 and 1 specifying the fraction of individuals in each subpopulation that migrates to a different subpopulation. See Migration Options  Scalar  
MigrationInterval  Positive integer specifying the number of generations that take place between migrations of individuals between subpopulations. See Migration Options.  Positive integer  
 I* Handle to the function that produces mutation children. See Mutation Options. 

 Nonlinear constraint algorithm. See Nonlinear Constraint Solver Algorithms. Option unchangeable
for For 

 Functions that For  Function handle or cell array of function handles  
 I* Scalar between 0
and 1 specifying the fraction of individuals to keep on the first
Pareto front while the solver selects individuals from higher fronts,
for  Scalar  
PenaltyFactor  NM I* Penalty update parameter.  Positive scalar  
 Function handle or cell array of function handles that plot data computed by the algorithm. See Plot Options. For 

PlotInterval  Positive integer specifying the number of generations between consecutive calls to the plot functions.  Positive integer  
 Size of the population.  Positive integer  
 Data type of the population. Must be 

 I* Handle to the function that selects parents of crossover and mutation children.


StallTest  NM Stopping test type. 

UseParallel  Compute fitness and nonlinear constraint functions in parallel. See Vectorize and Parallel Options (User Function Evaluation) and How to Use Parallel Processing. 

 Specifies whether functions are vectorized. See Vectorize and Parallel Options (User Function Evaluation) and Vectorize the Fitness Function. For 
