使用遗传算法优化自定义数据类型

function pop = create_permutations(NVARS,FitnessFcn,options)
%CREATE_PERMUTATIONS Creates a population of permutations.
%   POP = CREATE_PERMUTATION(NVARS,FITNESSFCN,OPTIONS) creates a population
%  of permutations POP each with a length of NVARS. 
%
%   The arguments to the function are 
%     NVARS: Number of variables 
%     FITNESSFCN: Fitness function 
%     OPTIONS: Options structure used by the GA

%   Copyright 2004-2007 The MathWorks, Inc.

totalPopulationSize = sum(options.PopulationSize);
n = NVARS;
pop = cell(totalPopulationSize,1);
for i = 1:totalPopulationSize
    pop{i} = randperm(n); 
end

function xoverKids  = crossover_permutation(parents,options,NVARS, ...
    FitnessFcn,thisScore,thisPopulation)
%   CROSSOVER_PERMUTATION Custom crossover function for traveling salesman.
%   XOVERKIDS = CROSSOVER_PERMUTATION(PARENTS,OPTIONS,NVARS, ...
%   FITNESSFCN,THISSCORE,THISPOPULATION) crossovers PARENTS to produce
%   the children XOVERKIDS.
%
%   The arguments to the function are 
%     PARENTS: Parents chosen by the selection function
%     OPTIONS: Options created from OPTIMOPTIONS
%     NVARS: Number of variables 
%     FITNESSFCN: Fitness function 
%     STATE: State structure used by the GA solver 
%     THISSCORE: Vector of scores of the current population 
%     THISPOPULATION: Matrix of individuals in the current population

%   Copyright 2004-2015 The MathWorks, Inc. 

nKids = length(parents)/2;
xoverKids = cell(nKids,1); % Normally zeros(nKids,NVARS);
index = 1;

for i=1:nKids
    % here is where the special knowledge that the population is a cell
    % array is used. Normally, this would be thisPopulation(parents(index),:);
    parent = thisPopulation{parents(index)};
    index = index + 2;

    % Flip a section of parent1.
    p1 = ceil((length(parent) -1) * rand);
    p2 = p1 + ceil((length(parent) - p1- 1) * rand);
    child = parent;
    child(p1:p2) = fliplr(child(p1:p2));
    xoverKids{i} = child; % Normally, xoverKids(i,:);
end

function mutationChildren = mutate_permutation(parents ,options,NVARS, ...
    FitnessFcn, state, thisScore,thisPopulation,mutationRate)
%   MUTATE_PERMUTATION Custom mutation function for traveling salesman.
%   MUTATIONCHILDREN = MUTATE_PERMUTATION(PARENTS,OPTIONS,NVARS, ...
%   FITNESSFCN,STATE,THISSCORE,THISPOPULATION,MUTATIONRATE) mutate the
%   PARENTS to produce mutated children MUTATIONCHILDREN.
%
%   The arguments to the function are 
%     PARENTS: Parents chosen by the selection function
%     OPTIONS: Options created from OPTIMOPTIONS
%     NVARS: Number of variables 
%     FITNESSFCN: Fitness function 
%     STATE: State structure used by the GA solver 
%     THISSCORE: Vector of scores of the current population 
%     THISPOPULATION: Matrix of individuals in the current population
%     MUTATIONRATE: Rate of mutation

%   Copyright 2004-2015 The MathWorks, Inc.

% Here we swap two elements of the permutation
mutationChildren = cell(length(parents),1);% Normally zeros(length(parents),NVARS);
for i=1:length(parents)
    parent = thisPopulation{parents(i)}; % Normally thisPopulation(parents(i),:)
    p = ceil(length(parent) * rand(1,2));
    child = parent;
    child(p(1)) = parent(p(2));
    child(p(2)) = parent(p(1));
    mutationChildren{i} = child; % Normally mutationChildren(i,:)
end

function scores = traveling_salesman_fitness(x,distances)
%TRAVELING_SALESMAN_FITNESS  Custom fitness function for TSP. 
%   SCORES = TRAVELING_SALESMAN_FITNESS(X,DISTANCES) Calculate the fitness 
%   of an individual. The fitness is the total distance traveled for an
%   ordered set of cities in X. DISTANCE(A,B) is the distance from the city
%   A to the city B.

%   Copyright 2004-2007 The MathWorks, Inc.

scores = zeros(size(x,1),1);
for j = 1:size(x,1)
    % here is where the special knowledge that the population is a cell
    % array is used. Normally, this would be pop(j,:);
    p = x{j}; 
    f = distances(p(end),p(1));
    for i = 2:length(p)
        f = f + distances(p(i-1),p(i));
    end
    scores(j) = f;
end

function state = traveling_salesman_plot(options,state,flag,locations)
%   TRAVELING_SALESMAN_PLOT Custom plot function for traveling salesman.
%   STATE = TRAVELING_SALESMAN_PLOT(OPTIONS,STATE,FLAG,LOCATIONS) Plot city
%   LOCATIONS and connecting route between them. This function is specific
%   to the traveling salesman problem.

%   Copyright 2004-2006 The MathWorks, Inc.
persistent x y xx yy
if strcmpi(flag,'init')
  load('usborder.mat','x','y','xx','yy');
end
plot(x,y,'Color','red');
axis([-0.1 1.5 -0.2 1.2]);

hold on;
[unused,i] = min(state.Score);
genotype = state.Population{i};

plot(locations(:,1),locations(:,2),'bo');
plot(locations(genotype,1),locations(genotype,2));
hold off

ga stopped because it exceeded options.MaxGenerations.

x =

  1x1 cell array

    {[14 12 36 3 5 11 40 25 38 37 7 30 28 10 23 21 27 4 1 ... ] (1x40 double)}


fval =

    5.3846


reason =

     0


output = 

  struct with fields:

      problemtype: 'unconstrained'
         rngstate: [1x1 struct]
      generations: 500
        funccount: 28563
          message: 'ga stopped because it exceeded options.MaxGenerations.'
    maxconstraint: []
       hybridflag: []