Optimization app has been removed in MATLAB Online?

I want to use "gatool", but it shows a message that "Optimization app has been removed". Please do the needful.

Answer 1

[Cris LaPierre]

gatool was removed in R2015b (see this answer).

Your observation is not limited to MATLAB Online. In R2021a, the optimization app has been removed. See the release notes:

• Under R2021a: The Optimization app (optimtool) has been removed. For a visual interface to solvers, use the Optimize Live Editor task.
• Under R2020b: The Optimize Live Editor task replaces the Optimization app (optimtool) as a visual interface for creating and solving optimization problems. As noted since R2015b, the Optimization app will be removed in a future release.

[Adil]

i dont know what i am doing,what i am downloading? i downloaded before General optimization tool,i need for genetic algoritms.what is alternative for ga tool

[Sam Chak]

@Adil, Have you tested if the ga() function is good enough?

x = ga(fun, nvars)

or

[x,fval,exitflag,output] = ga(fun, nvars, A, b, Aeq, beq, lb, ub, nonlcon, options)

See the documentation:

help ga

 GA    Constrained optimization using genetic algorithm.
    GA attempts to solve problems of the following forms:
        min F(X)  subject to:  A*X  <= B, Aeq*X  = Beq (linear constraints)
         X                     C(X) <= 0, Ceq(X) = 0 (nonlinear constraints)
                               LB <= X <= UB 
                               X(i) integer, where i is in the index
                               vector INTCON (integer constraints)
 
    Note: If INTCON is not empty, then no equality constraints are allowed.
    That is:-
    * Aeq and Beq must be empty
    * Ceq returned from NONLCON must be empty
 
    X = GA(FITNESSFCN,NVARS) finds a local unconstrained minimum X to the
    FITNESSFCN using GA. NVARS is the dimension (number of design
    variables) of the FITNESSFCN. FITNESSFCN accepts a vector X of size
    1-by-NVARS, and returns a scalar evaluated at X.
 
    X = GA(FITNESSFCN,NVARS,A,b) finds a local minimum X to the function
    FITNESSFCN, subject to the linear inequalities A*X <= B. Linear
    constraints are not satisfied when the PopulationType option is set to
    'bitString' or 'custom'. See the documentation for details.
 
    X = GA(FITNESSFCN,NVARS,A,b,Aeq,beq) finds a local minimum X to the
    function FITNESSFCN, subject to the linear equalities Aeq*X = beq as
    well as A*X <= B. (Set A=[] and B=[] if no inequalities exist.) Linear
    constraints are not satisfied when the PopulationType option is set to
    'bitString' or 'custom'. See the documentation for details.
 
    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. Use empty matrices for lb and ub if no bounds
    exist. Set lb(i) = -Inf if X(i) is unbounded below;  set ub(i) = Inf if
    X(i) is unbounded above. Linear constraints are not satisfied when the
    PopulationType option is set to 'bitString' or 'custom'. See the 
    documentation for details.
 
    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 the vectors C and Ceq, representing the
    nonlinear inequalities and equalities respectively. GA minimizes
    FITNESSFCN such that C(X)<=0 and Ceq(X)=0. (Set lb=[] and/or ub=[] if
    no bounds exist.) Nonlinear constraints are not satisfied when the
    PopulationType option is set to 'bitString' or 'custom'. See the 
    documentation for details.
 
    X = GA(FITNESSFCN,NVARS,A,b,Aeq,beq,lb,ub,NONLCON,options) minimizes
    with the default optimization parameters replaced by values in OPTIONS.
    OPTIONS can be created with the OPTIMOPTIONS function. See OPTIMOPTIONS
    for details. For a list of options accepted by GA refer to the
    documentation.
 
    X = GA(FITNESSFCN,NVARS,A,b,[],[],lb,ub,NONLCON,INTCON) requires that
    the variables listed in INTCON take integer values. Note that GA does
    not solve problems with integer and equality constraints. Pass empty
    matrices for the Aeq and beq inputs if INTCON is not empty.
 
    X = GA(FITNESSFCN,NVARS,A,b,[],[],lb,ub,NONLCON,INTCON,options)
    minimizes with integer constraints and the default optimization
    parameters replaced by values in OPTIONS. OPTIONS can be created with
    the OPTIMOPTIONS function. See OPTIMOPTIONS for details.
 
    X = GA(PROBLEM) finds the minimum for PROBLEM. PROBLEM is a structure
    that has the following fields:
        fitnessfcn: <Fitness function>
             nvars: <Number of design variables>
             Aineq: <A matrix for inequality constraints>
             bineq: <b vector for inequality constraints>
               Aeq: <Aeq matrix for equality constraints>
               beq: <beq vector for equality constraints>
                lb: <Lower bound on X>
                ub: <Upper bound on X>
           nonlcon: <Nonlinear constraint function>
            intcon: <Index vector for integer variables>
           options: <Options created with optimoptions('ga',...)>
          rngstate: <State of the random number generator>
 
    [X,FVAL] = GA(FITNESSFCN, ...) returns FVAL, the value of the fitness
    function FITNESSFCN at the solution X.
 
    [X,FVAL,EXITFLAG] = GA(FITNESSFCN, ...) returns EXITFLAG which
    describes the exit condition of GA. Possible values of EXITFLAG and the
    corresponding exit conditions are
 
      1 Average change in value of the fitness function over
         options.MaxStallGenerations generations less than 
         options.FunctionTolerance and constraint violation less than 
         options.ConstraintTolerance.
      3 The value of the fitness function did not change in
         options.MaxStallGenerations generations and constraint violation 
         less than options.ConstraintTolerance.
      4 Magnitude of step smaller than machine precision and constraint
         violation less than options.ConstraintTolerance. This exit 
         condition applies only to nonlinear constraints.
      5 Fitness limit reached and constraint violation less than
         options.ConstraintTolerance. 
      0 Maximum number of generations exceeded.
     -1 Optimization terminated by the output or plot function.
     -2 No feasible point found.
     -4 Stall time limit exceeded.
     -5 Time limit exceeded.
 
    [X,FVAL,EXITFLAG,OUTPUT] = GA(FITNESSFCN, ...) returns a
    structure OUTPUT with the following information:
              rngstate: <State of the random number generator before GA started>
           generations: <Total generations, excluding HybridFcn iterations>
             funccount: <Total function evaluations>
         maxconstraint: <Maximum constraint violation>, if any
               message: <GA termination message>
 
    [X,FVAL,EXITFLAG,OUTPUT,POPULATION] = GA(FITNESSFCN, ...) returns the
    final POPULATION at termination.
 
    [X,FVAL,EXITFLAG,OUTPUT,POPULATION,SCORES] = GA(FITNESSFCN, ...) returns
    the SCORES of the final POPULATION.
 
 
    Example:
      Unconstrained minimization of Rastrigins function:
        function scores = myRastriginsFcn(pop)
            scores = 10.0 * size(pop,2) + sum(pop.^2 - 10.0*cos(2*pi .* pop),2);
 
      numberOfVariables = 2
      x = ga(@myRastriginsFcn,numberOfVariables)
 
      Display plotting functions while GA minimizes
       options = optimoptions('ga','PlotFcn',...
         {@gaplotbestf,@gaplotbestindiv,@gaplotexpectation,@gaplotstopping});
       [x,fval,exitflag,output] = ga(fitfcn,2,[],[],[],[],[],[],[],options)
 
    An example with inequality constraints and lower bounds
     A = [1 1; -1 2; 2 1];  b = [2; 2; 3];  lb = zeros(2,1);
     fitfcn = @(x)0.5*x(1)^2 + x(2)^2 -x(1)*x(2) -2*x(1) - 6.0*x(2);
 
     % Use mutation function which can handle constraints
     options = optimoptions('ga','MutationFcn',@mutationadaptfeasible);
     [x,fval,exitflag] = ga(fitfcn,2,A,b,[],[],lb,[],[],options);
 
    If FITNESSFCN or NONLCON are parameterized, you can use anonymous
    functions to capture the problem-dependent parameters. Suppose you want
    to minimize the fitness given in the function myfit, subject to the
    nonlinear constraint myconstr, where these two functions are
    parameterized by their second argument a1 and a2, respectively. Here
    myfit and myconstr are MATLAB file functions such as
 
         function f = myfit(x,a1)
         f = exp(x(1))*(4*x(1)^2 + 2*x(2)^2 + 4*x(1)*x(2) + 2*x(2) + a1);
 
    and
 
         function [c,ceq] = myconstr(x,a2)
         c = [1.5 + x(1)*x(2) - x(1) - x(2);
               -x(1)*x(2) - a2];
         % No nonlinear equality constraints:
          ceq = [];
 
    To optimize for specific values of a1 and a2, first assign the values
    to these two parameters. Then create two one-argument anonymous
    functions that capture the values of a1 and a2, and call myfit and
    myconstr with two arguments. Finally, pass these anonymous functions to
    GA:
 
      a1 = 1; a2 = 10; % define parameters first
      % Mutation function for constrained minimization
      options = optimoptions('ga','MutationFcn',@mutationadaptfeasible);
      x = ga(@(x)myfit(x,a1),2,[],[],[],[],[],[],@(x)myconstr(x,a2),options)
 
    Example: Solving a mixed-integer optimization problem
    An example of optimizing a function where a subset of the variables are
    required to be integers:
    
    % Define the objective and call GA. Here variables x(2) and x(3) will
    % be integer. 
    fun = @(x) (x(1) - 0.2)^2 + (x(2) - 1.7)^2 + (x(3) -5.1)^2; 
    x = ga(fun,3,[],[],[],[],[],[],[],[2 3])
           
    See also OPTIMOPTIONS, FITNESSFUNCTION, GAOUTPUTFCNTEMPLATE, PATTERNSEARCH, @.

    Documentation for ga
       doc ga