floatexample.m

运用遗传算法的工具箱解决宽带雷达吸波结构的优化问题

echo on
% 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',2131415161)

% Crossover Operators
xFns = 'arithXover heuristicXover simpleXover';
xOpts = [2 0; 3 3; 2 0];

% Mutation Operators
mFns = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation';

mOpts = [3 0 0;4 120 3;4 120 3;3 0 0];

% Termination Operators
termFns = 'maxGenTerm';
termOps = [50]; % 100 Generations

% Selection Function
selectFn = 'normGeomSelect';
selectOps = [0.8];

% Evaluation Function
evalFn = 'gaRadarEval1';
evalOps = [];

% type gaRadarEval

% Bounds on the variables
bounds = [1 16; 0.001 10; 1 16; 0.001 10; 1 16; 0.001 10;1 16;0.001 10;1 16; 0.001 10];

% GA Options [epsilon float/binar display]
gaOpts=[1e-6 1 1];

% Generate an intialize population of size 100
startPop = initializega(600,bounds,'gaRadarEval1',[1e-6 1])


% Lets run the GA
% Hit a return to continue
pause

[x endPop1 bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
    termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);

% x is the best solution found
x
% Hit a return to continue
pause


% endPop is the ending population
endPop1
% Hit a return to continue
%pause

xFns = 'arithXover heuristicXover simpleXover';
xOpts = [40 0;60 4;40 0];

% Mutation Operators
mFns = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation';

mOpts = [5 0 0;6 30 3;6 30 3;5 0 0];

% Termination Operators
termFns = 'maxGenTerm';
termOps = [20]; % 100 Generations

% Selection Function
selectFn = 'normGeomSelect';
selectOps = [0.85];
evalFn = 'gaRadarEval1';
evalOps = [];
bounds = [1 16; 0.001 10; 1 16; 0.001 10; 1 16; 0.001 10;1 16;0.001 10;1 16; 0.001 10];
gaOpts=[1e-6 1 1];
startPop = endPop1
pause

[x endPop2 bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
    termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);

x
pause


% endPop is the ending population
endPop2

xFns = 'arithXover heuristicXover simpleXover';
xOpts = [6 0;8 4;6 0];

% Mutation Operators
mFns = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation';

mOpts = [8 0 0;9 30 3;9 30 3;8 0 0];

% Termination Operators
termFns = 'maxGenTerm';
termOps = [20]; % 100 Generations

% Selection Function
selectFn = 'normGeomSelect';
selectOps = [0.75];
evalFn = 'gaRadarEval1';
evalOps = [];
bounds = [1 16; 0.001 10; 1 16; 0.001 10; 1 16; 0.001 10; 1 16; 0.001 10; 1 16; 0.001 10];
gaOpts=[1e-6 1 1];
startPop = endPop2
pause

[x endPop3 bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
    termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);

x
pause


% endPop is the ending population
endPop3


xFns = 'arithXover heuristicXover simpleXover';
xOpts = [8 0;9 4; 8 0];

% Mutation Operators
mFns = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation';

mOpts = [9 0 0;10 30 3;10 30 3;9 0 0];

% Termination Operators
termFns = 'maxGenTerm';
termOps = [100]; % 100 Generations

% Selection Function
selectFn = 'normGeomSelect';
selectOps = [0.85];
evalFn = 'gaRadarEval1';
evalOps = [];
bounds = [1 16; 0.001 10; 1 16; 0.001 10; 1 16; 0.001 10; 1 16; 0.001 10; 1 16;  0.001 10];
gaOpts=[1e-6 1 1];
startPop = endPop3
pause

[x endPop4 bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
    termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);

x
pause


% endPop is the ending population
endPop4
% bestPop is the best solution tracked over generations
% bestPop is the best solution tracked over generations
%bestPop
% Hit a return to continue
%pause


% trace is a trace of the best value and average value of generations
%trace
% Hit a return to continue
%pause


% Plot the best over time
%clf
%plot(trace(:,1),trace(:,2));
% Hit a return to continue
%pause


% Add the average to the graph
%hold on
%plot(trace(:,1),trace(:,3));
% Hit a return to continue
%pause


% Lets increase the population size by running the defaults
% 

%[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,[],gaOpts);

% x is the best solution found
%x
% Hit a return to continue
%pause


% endPop is the ending population
%endPop
% Hit a return to continue
%pause


% bestPop is the best solution tracked over generations
%bestPop
% Hit a return to continue
%pause


% trace is a trace of the best value and average value of generations
%trace
% Hit a return to continue
%pause


% Plot the best over time
%clf
%plot(trace(:,1),trace(:,2));
% Hit a return to continue
%pause


% Add the average to the graph
%hold on
%plot(trace(:,1),trace(:,3));


echo off