gaoptimset

(Not recommended) Create genetic algorithm options structure

gaoptimset is not recommended. Use optimoptions instead. For more information, see Compatibility Considerations.

Syntax

gaoptimset
options = gaoptimset
options = gaoptimset(@ga)
options = gaoptimset(@gamultiobj)
options = gaoptimset('param1',value1,'param2',value2,...)
options = gaoptimset(oldopts,'param1',value1,...)
options = gaoptimset(oldopts,newopts)


Description

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.

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. {}* 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, max(sqrt(eps),ConstraintTolerance) determines feasibility with respect to linear constraints. For an options structure, use TolCon.	Positive scalar | {1e-3}
CreationFcn	I* Function that creates the initial population. Specify as a name of a built-in creation function or a function handle. See Population Options.	{'gacreationuniform'} | {'gacreationlinearfeasible'}* | Custom creation function
CrossoverFcn	I* Function that the algorithm uses to create crossover children. Specify as a name of a built-in crossover function or a function handle. See Crossover Options.	{'crossoverscattered'} for ga, {'crossoverintermediate'}* for gamultiobj | 'crossoverheuristic' | 'crossoversinglepoint' | 'crossovertwopoint' | 'crossoverarithmetic' | Custom crossover function
CrossoverFraction	The fraction of the population at the next generation, not including elite children, that the crossover function creates.	Positive scalar | {0.8}
Display	Level of display.	'off' | 'iter' | 'diagnose' | {'final'}
DistanceMeasureFcn	Function that computes distance measure of individuals. Specify as a name of a built-in distance measure function or a function handle. The value applies to decision variable or design space (genotype) or to function space (phenotype). The default 'distancecrowding' is in function space (phenotype). For gamultiobj only. See Multiobjective Options. For an options structure, use a function handle, not a name.	{'distancecrowding'} means the same as {@distancecrowding,'phenotype'} | {@distancecrowding,'genotype'} | Custom distance function
EliteCount	NM Positive integer specifying how many individuals in the current generation are guaranteed to survive to the next generation. Not used in gamultiobj.	Positive integer | {ceil(0.05*PopulationSize)} | {0.05*(default PopulationSize)} for mixed-integer problems
FitnessLimit	NM If the fitness function attains the value of FitnessLimit, the algorithm halts.	Scalar | {-Inf}
FitnessScalingFcn	Function that scales the values of the fitness function. Specify as a name of a built-in scaling function or a function handle. Option unavailable for gamultiobj.	{'fitscalingrank'} | 'fitscalingshiftlinear' | 'fitscalingprop' | 'fitscalingtop' | Custom fitness scaling function
FunctionTolerance	The algorithm stops if the average relative change in the best fitness function value over MaxStallGenerations generations is less than or equal to FunctionTolerance. If StallTest is 'geometricWeighted', then the algorithm stops if the weighted average relative change is less than or equal to FunctionTolerance. For gamultiobj, the algorithm stops when the geometric average of the relative change in value of the spread over options.MaxStallGenerations generations is less than options.FunctionTolerance, and the final spread is less than the mean spread over the past options.MaxStallGenerations generations. See gamultiobj Algorithm. For an options structure, use TolFun.	Positive scalar | {1e-6} for ga, {1e-4} for gamultiobj
HybridFcn	I* Function that continues the optimization after ga terminates. Specify as a name or a function handle. Alternatively, a cell array specifying the hybrid function and its options. See ga Hybrid Function. For gamultiobj, the only hybrid function is @fgoalattain. See gamultiobj Hybrid Function. See When to Use a Hybrid Function.	Function name or handle | 'fminsearch' | 'patternsearch' | 'fminunc' | 'fmincon' | {[]} or 1-by-2 cell array | {@solver, hybridoptions}, where solver = fminsearch, patternsearch, fminunc, or fmincon {[]}
InitialPenalty	NM I* Initial value of penalty parameter	Positive scalar | {10}
InitialPopulationMatrix	Initial population used to seed the genetic algorithm. Has up to PopulationSize rows and N columns, where N is the number of variables. You can pass a partial population, meaning one with fewer than PopulationSize rows. In that case, the genetic algorithm uses CreationFcn to generate the remaining population members. See Population Options For an options structure, use InitialPopulation.	Matrix | {[]}
InitialPopulationRange	Matrix or vector specifying the range of the individuals in the initial population. Applies to gacreationuniform creation function. ga shifts and scales the default initial range to match any finite bounds. For an options structure, use PopInitRange.	Matrix or vector | {[-10;10]} for unbounded components, {[-1e4+1;1e4+1]} for unbounded components of integer-constrained problems, {[lb;ub]} for bounded components, with the default range modified to match one-sided bounds.
InitialScoresMatrix	I* Initial scores used to determine fitness. Has up to PopulationSize rows and has Nf columns, where Nf is the number of fitness functions (1 for ga, greater than 1 for gamultiobj). You can pass a partial scores matrix, meaning one with fewer than PopulationSize rows. In that case, the solver fills in the scores when it evaluates the fitness functions. For an options structure, use InitialScores.	Column vector for single objective | matrix for multiobjective | {[]}
MaxGenerations	Maximum number of iterations before the algorithm halts. For an options structure, use Generations.	Positive integer |{100*numberOfVariables} for ga, {200*numberOfVariables} for gamultiobj
MaxStallGenerations	The algorithm stops if the average relative change in the best fitness function value over MaxStallGenerations generations is less than or equal to FunctionTolerance. If StallTest is 'geometricWeighted', then the algorithm stops if the weighted average relative change is less than or equal to FunctionTolerance. For gamultiobj, the algorithm stops when the geometric average of the relative change in value of the spread over options.MaxStallGenerations generations is less than options.FunctionTolerance, and the final spread is less than the mean spread over the past options.MaxStallGenerations generations. See gamultiobj Algorithm. For an options structure, use StallGenLimit.	Positive integer | {50} for ga, {100} for gamultiobj
MaxStallTime	NM The algorithm stops if there is no improvement in the objective function for MaxStallTime seconds, as measured by tic and toc. For an options structure, use StallTimeLimit.	Positive scalar | {Inf}
MaxTime	The algorithm stops after running after MaxTime seconds, as measured by tic and toc. This limit is enforced after each iteration, so ga can exceed the limit when an iteration takes substantial time. For an options structure, use TimeLimit.	Positive scalar | {Inf}
MigrationDirection	Direction of migration. See Migration Options	'both' | {'forward'}
MigrationFraction	Scalar from 0 through 1 specifying the fraction of individuals in each subpopulation that migrates to a different subpopulation. See Migration Options	Scalar | {0.2}
MigrationInterval	Positive integer specifying the number of generations that take place between migrations of individuals between subpopulations. See Migration Options.	Positive integer | {20}
MutationFcn	I* Function that produces mutation children. Specify as a name of a built-in mutation function or a function handle. See Mutation Options.	{'mutationgaussian'} for ga, {'mutationadaptfeasible'}* for gamultiobj | 'mutationuniform' | Custom mutation function
NonlinearConstraintAlgorithm	Nonlinear constraint algorithm. See Nonlinear Constraint Solver Algorithms. Option unchangeable for gamultiobj. For an options structure, use NonlinConAlgorithm.	{'auglag'} for ga, {'penalty'} for gamultiobj
OutputFcn	Functions that ga calls at each iteration. Specify as a function handle or a cell array of function handles. See Output Function Options. For an options structure, use OutputFcns.	Function handle or cell array of function handles | {[]}
ParetoFraction	Scalar from 0 through 1 specifying the fraction of individuals to keep on the first Pareto front while the solver selects individuals from higher fronts, for gamultiobj only. See Multiobjective Options.	Scalar | {0.35}
PenaltyFactor	NM I* Penalty update parameter.	Positive scalar | {100}
PlotFcn	Function that plots data computed by the algorithm. Specify as a name of a built-in plot function, a function handle, or a cell array of built-in names or function handles. See Plot Options. For an options structure, use PlotFcns.	ga or gamultiobj: {[]} | 'gaplotdistance' | 'gaplotgenealogy' | 'gaplotselection' | 'gaplotscorediversity' |'gaplotscores' | 'gaplotstopping' | 'gaplotmaxconstr' | Custom plot function ga only: 'gaplotbestf' | 'gaplotbestindiv' | 'gaplotexpectation' | 'gaplotrange' gamultiobj only: 'gaplotpareto' | 'gaplotparetodistance' | 'gaplotrankhist' | 'gaplotspread'
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 | {50} when numberOfVariables <= 5, {200} otherwise | {min(max(10*nvars,40),100)} for mixed-integer problems
PopulationType	Data type of the population. Must be 'doubleVector' for mixed integer problems.	'bitstring' | 'custom' | {'doubleVector'} ga ignores all constraints when PopulationType is set to 'bitString' or 'custom'. See Population Options.
SelectionFcn	I* Function that selects parents of crossover and mutation children. Specify as a name of a built-in selection function or a function handle. gamultiobj uses only 'selectiontournament'.	{'selectionstochunif'} for ga, {'selectiontournament'} for gamultiobj | 'selectionremainder' | 'selectionuniform' | 'selectionroulette' | Custom selection function
StallTest	NM Stopping test type.	'geometricWeighted' | {'averageChange'}
UseParallel	Compute fitness and nonlinear constraint functions in parallel. See Vectorize and Parallel Options (User Function Evaluation) and How to Use Parallel Processing in Global Optimization Toolbox.	true | {false}
UseVectorized	Specifies whether functions are vectorized. See Vectorize and Parallel Options (User Function Evaluation) and Vectorize the Fitness Function. For an options structure, use Vectorized with the values 'on' or 'off'.	true | {false}

Compatibility Considerations

gaoptimset is not recommended

Not recommended starting in R2018b

To set options, the gaoptimset, psoptimset, and saoptimset functions are not recommended. Instead, use optimoptions.

The only difference between using optimoptions and the other functions is, for optimoptions, you include the solver name as the first argument. For example, to set iterative display in ga,

options = optimoptions('ga','Display','iter');
% instead of
options = gaoptimset('Display','iter');

optimoptions has several advantages over the other functions.

• optimoptions has better automatic code suggestions and completions, especially in the Live Editor.

• You can use a single option-setting function instead of a variety of functions.

There are no plans to remove gaoptimset, psoptimset, and saoptimset at this time.