gaoptimset - Create genetic algorithm options structure

CreationFcn

Handle to the function that creates the initial population

@gacreationuniform | @gacreationlinearfeasible

CrossoverFcn

Handle to the function that the algorithm uses to create crossover children

@crossoverheuristic {@crossoverscattered} @crossoverintermediate @crossoversinglepoint @crossovertwopoint @crossoverarithmetic

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}

Display

Level of display

'off' | 'iter' | 'diagnose' | {'final'}

DistanceMeasureFcn

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

{@distancecrowding,'phenotype'}

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

@fitscalingshiftlinear @fitscalingprop @fitscalingtop {@fitscalingrank}

Generations

Positive integer specifying the maximum number of iterations before the algorithm halts

Positive integer |{100}

HybridFcn

Handle to a function that continues the optimization after ga terminates

or

Cell array specifying the hybrid function and its options structure

Function handle | @fminsearch @patternsearch @fminunc @fmincon {[]}

or

1-by-2 cell array | {@solver, hybridoptions}, where solver = fminsearch, patternsearch, fminunc, or fmincon {[]}

InitialPenalty

Initial value of penalty parameter

Positive scalar | {10}

InitialPopulation

Initial population used to seed the genetic algorithm; can be partial

Matrix | {[]}

InitialScores

Initial scores used to determine fitness; can be partial

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 @mutationadaptfeasible {@mutationgaussian}

OutputFcns

Functions that ga calls at each iteration

@gaoutputgen | {[]}

ParetoFraction

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 | {0.35}

PenaltyFactor

Penalty update parameter

Positive scalar | {100}

PlotFcns

Array of handles to functions that plot data computed by the algorithm

@gaplotbestf @gaplotbestindiv @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}

PopInitRange

Matrix or vector specifying the range of the individuals in the initial population

Matrix or vector | [0;1]

PopulationSize

Size of the population

Positive integer | {20}

PopulationType

String describing the data type of the population

'bitstring' | 'custom' | {'doubleVector'}

Note that linear and nonlinear constraints are not satisfied when PopulationType is set to 'bitString' or 'custom'.

SelectionFcn

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

@selectionremainder @selectionuniform {@selectionstochunif} @selectionroulette @selectiontournament

StallGenLimit

Positive integer. The algorithm stops if there is no improvement in the objective function for StallGenLimit consecutive generations.

Positive integer | {50}

StallTimeLimit

Positive scalar. The algorithm stops if there is no improvement in the objective function for StallTimeLimit seconds.

Positive scalar | {Inf}

TimeLimit

Positive scalar. The algorithm stops after running for TimeLimit seconds.

Positive scalar | {Inf}

Positive scalar. TolCon is used to determine the feasibility with respect to nonlinear constraints.

Positive scalar | {1e-6}

Positive scalar. The algorithm runs until the cumulative change in the fitness function value over StallGenLimit is less than TolFun.

Positive scalar | {1e-6}

Compute fitness functions of a population in parallel.

'always' | {'never'}

Vectorized

String specifying whether the computation of the fitness function is vectorized

'on' | {'off'}