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.
Option  Description  Values 

 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 

 I* Handle to the function that computes distance measure of individuals, computed in decision variable or design space (genotype) or in function space (phenotype) 

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

 Positive integer specifying the maximum number of iterations before the algorithm halts  Positive integer  
 I* Handle to a function
that continues the optimization after or Cell array specifying the hybrid function and its options structure  Function handle  or 1by2
cell array  
 I* Initial value of penalty parameter  Positive scalar  
 Initial population used to seed the genetic algorithm; can be partial  Matrix  
 I* Initial scores used to determine fitness; can be partial  Column vector  
 Direction of migration — see Migration Options 

 Scalar between 0 and 1 specifying the fraction of individuals in each subpopulation that migrates to a different subpopulation — see Migration Options  Scalar  
 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. 

 Functions that  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  Scalar  
 I* Penalty update parameter  Positive scalar  
 Array of handles to functions that plot data computed by the algorithm. See Plot Options. 
For 
 Positive integer specifying the number of generations between consecutive calls to the plot functions  Positive integer  
 Matrix or vector specifying the range of the individuals
in the initial population. Applies to  Matrix or vector  
 Size of the population  Positive integer  
 String describing the data type of the population —
must be 

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

 Positive integer. The algorithm stops if the average
relative change in the best fitness function value over  Positive integer  
 String describing the stopping test. 

 Positive scalar. The algorithm stops if there is no improvement
in the objective function for  Positive scalar 
 Positive scalar. The algorithm stops after running for  Positive scalar  
TolCon  Positive scalar.  Positive scalar  
TolFun  Positive scalar. The algorithm stops if the average relative
change in the best fitness function value over  Positive scalar  
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. 

ga
 gamultiobj
 gaoptimget