floatexample.m

genetic遗传算法

% This script shows how to use the ga using a float representation. 
% You should see the demos for
% more information as well. gademo1, gademo2, gademo3
global bounds
% Setting the seed to the same for binary
rand('seed',156789)
% Crossover Operators
xFns = 'arithXover heuristicXover simpleXover';
xOpts = [1 0; 1 3; 1 0];
% Mutation Operators
mFns = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation';
mOpts = [2 0 0;3 200 3;2 200 3;2 0 0];
% Termination Operators
termFns = 'maxGenTerm';
termOps = [200]; % 200 Generations
% Selection Function
selectFn = 'normGeomSelect';
selectOps = [0.08];
% Evaluation Function
evalFn = 'gaMichEval';
evalOps = [];
% Bounds on the variables
bounds = [-3 12.1; 4.1 5.8];
% GA Options [epsilon float/binar display]
gaOpts=[1e-6 1 0];
% Generate an intialize population of size 20
startPop = initializega(20,bounds,evalFn,[1e-6 1]);
% Lets run the GA
% Hit a return to continue
[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
    termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);
% x is the best solution found
x
% % endPop is the ending population
% endPop
% % bestPop is the best solution tracked over generations
% bestPop
% % trace is a trace of the best value and average value of generations
% trace
% % Plot the best over time
clf
plot(trace(:,1),trace(:,2));
hold on
plot(trace(:,1),trace(:,3));
[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,[],gaOpts);
% x is the best solution found
x
% % endPop is the ending population
% endPop
% % bestPop is the best solution tracked over generations
% bestPop
% % trace is a trace of the best value and average value of generations
% trace
% % Plot the best over time
figure
plot(trace(:,1),trace(:,2));
hold on
plot(trace(:,1),trace(:,3));