% This script shows how to use the ga using a float representation. % You should see the demos for% more information as well. gademo1, gademo2, gademo3global bounds% Setting the seed back to the beginning for comparison sakerand('seed',0)% Crossover OperatorsxFns = 'simpleXover';xOpts = [.4];% Mutation OperatorsmFns = 'binaryMutation';mOpts = [0.005];% Termination OperatorstermFns = 'maxGenTerm';termOps = [200]; % 200 Generations% Selection FunctionselectFn = 'roulette'selectOps = [];% Evaluation FunctionevalFn = 'gaMichEval';evalOps = [];type gaMichEval% Bounds on the variablesbounds = [-3 12.1; 4.1 5.8];% GA Options [epsilon float/binar display]gaOpts=[1e-6 0 1];% Generate an intialize population of size 20startPop = initialize(20,bounds,'gaMichEval',[],[1e-6 0]);% Lets run the GApause[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...    termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);pause% x is the best solution foundxpause% endPop is the ending populationendPoppause% trace is a trace of the best value and average value of generationstracepause% Plot the best over timeclgplot(trace(:,1),trace(:,2));pause% Add the average to the graphhold onplot(trace(:,1),trace(:,3));pause% Lets increase the population size by running the defaults% termOps=[100];[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,[],gaOpts,termFns,termOps,...    selectFn,selectOps);% x is the best solution foundxpause% endPop is the ending populationendPoppause% trace is a trace of the best value and average value of generationstracepause% Plot the best over timeclgplot(trace(:,1),trace(:,2));pause% Add the average to the graphhold onplot(trace(:,1),trace(:,3));pause