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ga

Find minimum of function using genetic algorithm

Syntax

x = ga(fitnessfcn,nvars)
x = ga(fitnessfcn,nvars,A,b)
x = ga(fitnessfcn,nvars,A,b,Aeq,beq)
x = ga(fitnessfcn,nvars,A,b,Aeq,beq,LB,UB)
x = ga(fitnessfcn,nvars,A,b,Aeq,beq,LB,UB,nonlcon)
x = ga(fitnessfcn,nvars,A,b,Aeq,beq,LB,UB,nonlcon,options)
x = ga(fitnessfcn,nvars,A,b,[],[],LB,UB,nonlcon,IntCon)
x = ga(fitnessfcn,nvars,A,b,[],[],LB,UB,nonlcon,IntCon,options)
x = ga(problem)
[x,fval] = ga(fitnessfcn,nvars,...)
[x,fval,exitflag] = ga(fitnessfcn,nvars,...)
[x,fval,exitflag,output] = ga(fitnessfcn,nvars,...)
[x,fval,exitflag,output,population] = ga(fitnessfcn,nvars,...)
[x,fval,exitflag,output,population,scores] = ga(fitnessfcn,nvars,...)

Description

x = ga(fitnessfcn,nvars) finds a local unconstrained minimum, x, to the objective function, fitnessfcn. nvars is the dimension (number of design variables) of fitnessfcn.

x = ga(fitnessfcn,nvars,A,b) finds a local minimum x to fitnessfcn, subject to the linear inequalities A*x ≤ b. ga evaluates the matrix product A*x as if x is transposed (A*x').

x = ga(fitnessfcn,nvars,A,b,Aeq,beq) finds a local minimum x to fitnessfcn, subject to the linear equalities Aeq*x = beq as well as A*x ≤ b. (Set A=[] and b=[] if no linear inequalities exist.) ga evaluates the matrix product Aeq*x as if x is transposed (Aeq*x').

x = ga(fitnessfcn,nvars,A,b,Aeq,beq,LB,UB) defines a set of lower and upper bounds on the design variables, x, so that a solution is found in the range LB  x  UB. (Set Aeq=[] and beq=[] if no linear equalities exist.)

x = ga(fitnessfcn,nvars,A,b,Aeq,beq,LB,UB,nonlcon) subjects the minimization to the constraints defined in nonlcon. The function nonlcon accepts x and returns vectors C and Ceq, representing the nonlinear inequalities and equalities respectively. ga minimizes the fitnessfcn such that C(x)  0 and Ceq(x) = 0. (Set LB=[] and UB=[] if no bounds exist.)

x = ga(fitnessfcn,nvars,A,b,Aeq,beq,LB,UB,nonlcon,options) minimizes with the default optimization parameters replaced by values in the structure options, which can be created using the gaoptimset function. (Set nonlcon=[] if no nonlinear constraints exist.)

x = ga(fitnessfcn,nvars,A,b,[],[],LB,UB,nonlcon,IntCon) requires that the variables listed in IntCon take integer values.

    Note:   When there are integer constraints, ga does not accept linear or nonlinear equality constraints, only inequality constraints.

x = ga(fitnessfcn,nvars,A,b,[],[],LB,UB,nonlcon,IntCon,options) minimizes with integer constraints and with the default optimization parameters replaced by values in the options structure.

x = ga(problem) finds the minimum for problem, where problem is a structure.

[x,fval] = ga(fitnessfcn,nvars,...) returns fval, the value of the fitness function at x.

[x,fval,exitflag] = ga(fitnessfcn,nvars,...) returns exitflag, an integer identifying the reason the algorithm terminated.

[x,fval,exitflag,output] = ga(fitnessfcn,nvars,...) returns output, a structure that contains output from each generation and other information about the performance of the algorithm.

[x,fval,exitflag,output,population] = ga(fitnessfcn,nvars,...) returns the matrix, population, whose rows are the final population.

[x,fval,exitflag,output,population,scores] = ga(fitnessfcn,nvars,...) returns scores the scores of the final population.

Input Arguments

fitnessfcn

Handle to the fitness function. The fitness function should accept a row vector of length nvars and return a scalar value.

When the 'Vectorized' option is 'on', fitnessfcn should accept a pop-by-nvars matrix, where pop is the current population size. In this case fitnessfcn should return a vector the same length as pop containing the fitness function values. fitnessfcn should not assume any particular size for pop, since ga can pass a single member of a population even in a vectorized calculation.

nvars

Positive integer representing the number of variables in the problem.

A

Matrix for linear inequality constraints of the form

A*x ≤ b.

If the problem has m linear inequality constraints and nvars variables, then

  • A is a matrix of size m-by-nvars.

  • b is a vector of length m.

ga evaluates the matrix product A*x as if x is transposed (A*x').

    Note:   ga does not enforce linear constraints to be satisfied when the PopulationType option is 'bitString' or 'custom'.

b

Vector for linear inequality constraints of the form

A*x ≤ b.

If the problem has m linear inequality constraints and nvars variables, then

  • A is a matrix of size m-by-nvars.

  • b is a vector of length m.

Aeq

Matrix for linear equality constraints of the form

Aeq*x = beq.

If the problem has m linear equality constraints and nvars variables, then

  • Aeq is a matrix of size m-by-nvars.

  • beq is a vector of length m.

ga evaluates the matrix product Aeq*x as if x is transposed (Aeq*x').

    Note:   ga does not enforce linear constraints to be satisfied when the PopulationType option is 'bitString' or 'custom'.

beq

Vector for linear equality constraints of the form

Aeq*x = beq.

If the problem has m linear equality constraints and nvars variables, then

  • Aeq is a matrix of size m-by-nvars.

  • beq is a vector of length m.

LB

Vector of lower bounds. ga enforces that iterations stay above LB. Set LB(i) = –Inf if x(i)is unbounded below.

    Note:   ga does not enforce bounds to be satisfied when the PopulationType option is 'bitString' or 'custom'.

UB

Vector of upper bounds. ga enforces that iterations stay below UB. Set UB(i) = Inf if x(i) is unbounded above.

nonlcon

Function handle that returns two outputs:

[c,ceq] = nonlcon(x)

ga attempts to achieve c ≤ 0 and ceq = 0. c and ceq are row vectors when there are multiple constraints. Set unused outputs to [].

You can write nonlcon as a function handle to a file, such as

nonlcon = @constraintfile

where constraintfile.m is a file on your MATLAB® path.

To learn how to use vectorized constraints, see Vectorized Constraints.

    Note:   ga does not enforce nonlinear constraints to be satisfied when the PopulationType option is set to 'bitString' or 'custom'.

    If IntCon is not empty, the second output of nonlcon (ceq) must be empty ([]).

    For information on how ga uses nonlcon, see Nonlinear Constraint Solver Algorithm.

options

Structure containing optimization options. Create options using gaoptimset, or by exporting options from the Optimization app as described in Importing and Exporting Your Work in the Optimization Toolbox™ documentation.

IntCon

Vector of positive integers taking values from 1 to nvars. Each value in IntCon represents an x component that is integer-valued.

problem

Structure containing the following fields:

fitnessfcn

Fitness function

nvars

Number of design variables

Aineq

A matrix for linear inequality constraints

Bineq

b vector for linear inequality constraints

Aeq

Aeq matrix for linear equality constraints

Beq

beq vector for linear equality constraints

lb

Lower bound on x

ub

Upper bound on x

nonlcon

Nonlinear constraint function

rngstate

Optional field to reset the state of the random number generator

intcon

Index vector of integer variables

solver

'ga'

options

Options structure created using gaoptimset or the Optimization app

Create problem by exporting a problem from the Optimization app, as described in Importing and Exporting Your Work in the Optimization Toolbox documentation.

Output Arguments

x

Best point that ga located during its iterations.

fval

Fitness function evaluated at x.

exitflag

Integer giving the reason ga stopped iterating:

Exit FlagMeaning
1

Without nonlinear constraints — Average cumulative change in value of the fitness function over StallGenLimit generations is less than TolFun, and the constraint violation is less than TolCon.

With nonlinear constraints — Magnitude of the complementarity measure (see Definitions) is less than sqrt(TolCon), the subproblem is solved using a tolerance less than TolFun, and the constraint violation is less than TolCon.

2

Fitness limit reached and the constraint violation is less than TolCon.

3

Value of the fitness function did not change in StallGenLimit generations and the constraint violation is less than TolCon.

4

Magnitude of step smaller than machine precision and the constraint violation is less than TolCon.

5

Minimum fitness limit FitnessLimit reached and the constraint violation is less than TolCon.

0

Maximum number of generations Generations exceeded.

-1

Optimization terminated by an output function or plot function.

-2

No feasible point found.

-4

Stall time limit StallTimeLimit exceeded.

-5

Time limit TimeLimit exceeded.

When there are integer constraints, ga uses the penalty fitness value instead of the fitness value for stopping criteria.

output

Structure containing output from each generation and other information about algorithm performance. The output structure contains the following fields:

  • problemtype — String describing the type of problem, one of:

    • 'unconstrained'

    • 'boundconstraints'

    • 'linearconstraints'

    • 'nonlinearconstr'

    • 'integerconstraints'

  • rngstate — State of the MATLAB random number generator, just before the algorithm started. You can use the values in rngstate to reproduce the output of ga. See Reproduce Results.

  • generations — Number of generations computed.

  • funccount — Number of evaluations of the fitness function.

  • message — Reason the algorithm terminated.

  • maxconstraint — Maximum constraint violation, if any.

population

Matrix whose rows contain the members of the final population.

scores

Column vector of the fitness values (scores for integerconstraints problems) of the final population.

Examples

Given the following inequality constraints and lower bounds

use this code to find the minimum of the lincontest6 function, which is provided in your software:

A = [1 1; -1 2; 2 1];
b = [2; 2; 3];
lb = zeros(2,1);
[x,fval,exitflag] = ga(@lincontest6,... 
    2,A,b,[],[],lb)

Optimization terminated: average change in
the fitness value less than options.TolFun.

x =
    0.6700    1.3310

fval =
   -8.2218

exitflag =
     1
 

Optimize a function where some variables must be integers:

fun = @(x) (x(1) - 0.2)^2 + ...
    (x(2) - 1.7)^2 + (x(3) - 5.1)^2;
x = ga(fun,3,[],[],[],[],[],[],[], ...
   [2 3]) % variables 2 and 3 are integers

Optimization terminated: average change
in the penalty fitness value less
than options.TolFun and constraint violation
is less than options.TolCon.

x =
    0.2000    2.0000    5.0000

Alternatives

For problems without integer constraints, consider using patternsearch instead of ga.

More About

expand all

Complementarity Measure

In the nonlinear constraint solver, the complementarity measure is the norm of the vector whose elements are ciλi, where ci is the nonlinear inequality constraint violation, and λi is the corresponding Lagrange multiplier.

Tips

  • To write a function with additional parameters to the independent variables that can be called by ga, see Passing Extra Parameters in the Optimization Toolbox documentation.

  • For problems that use the population type Double Vector (the default), ga does not accept functions whose inputs are of type complex. To solve problems involving complex data, write your functions so that they accept real vectors, by separating the real and imaginary parts.

Algorithms

For a description of the genetic algorithm, see How the Genetic Algorithm Works.

For a description of the mixed integer programming algorithm, see Integer ga Algorithm.

For a description of the nonlinear constraint algorithm, see Nonlinear Constraint Solver Algorithm.

References

[1] Goldberg, David E., Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley, 1989.

[2] A. R. Conn, N. I. M. Gould, and Ph. L. Toint. "A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds", SIAM Journal on Numerical Analysis, Volume 28, Number 2, pages 545–572, 1991.

[3] A. R. Conn, N. I. M. Gould, and Ph. L. Toint. "A Globally Convergent Augmented Lagrangian Barrier Algorithm for Optimization with General Inequality Constraints and Simple Bounds", Mathematics of Computation, Volume 66, Number 217, pages 261–288, 1997.

See Also

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