This is the old template-directory README. Most of this information isstill accurate and it contains more details than the new README.However, see there first for the creation of new projects.========================================================================This directory contains sample files that should make it easy tocreate an EO algorithm to evolve any type of structure (EO comes with two examples, bitstrings and vector of real variables,so you'll need this as soon as you want to evolve something else).At the moment, only algorithms involving a scalar fitness (double)are implemented (see test dir for Pareto optimization of multiple-objective fitness - or be patient :-)This file will help you to build the same algorithm than the ones in the Lesson4 of the tutorial, but with YOUR genotype instead ofbitstrings or vector<double>. More details in Lesson5 of the tutorial.It is assumed in the following that you have read the first part ofthe tutorial (Lessons 1 to 4).Creating the algorithm for your genotype----------------------------------------In what follows, we will suppose that you want to evolve some datastructure, and that you have enough programming skills to be able towrite C code for its random initilialization, its crossover, itsmutation and the computation of its fitness.The helper script * create.sh * will create for you the files you needfrom the samples in tutorial/Templates dir, and all you'll have to dois to include the actual code where indicated in those files (betweenkeywords START and END). First, let's choose a name: let's call the new EO class eoAppli.All newly created classes will be named eoAppliXXX (in the fileeoAppliXXX)1- cd to the tutorial dir2- create the directory for your application (let's assume you call itAPPLICATION): type in            mkdir APPLICATION3- go to the Templates dir             cd Templatesand run the helper script create.sh with the following arguments             ./create.sh Appli ../APPLICATION4- cd to the APPLICATION dir (cd ../APPLICATION). You should see there the following files:   AppliEA.cpp            the main file, includes all other, to be compiled   Makefile               with default target eoAppliEA   eoAppli.h              class eoAppli<FitT>, FitT = template fitness   eoAppliEvalFunc.h      class for the computation of fotness   eoAppliInit.h          class for genotype initlialization   eoAppliMutation.h      class for mutation    eoAppliQuadCrossover.h class for (quadratic) crossover   make_genotype_Appli.h  helper function that create the initializer   make_op_Appli.h        helper function that creates the variatin operatorsNote: You can go directly to step 6 and 7: you'll get a lot ofwarnings, but will be able to run an EA that does nothing!5- Edit those files to suit your needs. The minimal addition you'll needto make are   in eoAppli.h              define your genotype   in eoAppliInit.h          define the initialization of one genotype   in eoAppliMutation.h      define the mutation of one genotype   in eoAppliQuadCrossover.h define the crossover of 2 genotypesHINT: look for keywords START and END and modify code in between.6- Compile eoAppliEA.cpp. If your APPLICATION dir is in the tutorialdir, you don't need to modify Makefile. Just type in               % make7- Run the resulting program:               % eoAppliEAThe default output is one line per generation with the generationnumber, the number of evaluations performed, the best and averagefitnesses in the population.The algorithm stops by default after 100 generations.8- Customize the parameters: copy eoAppliEA.status intoe.g. eoAppliEA.param, edit eoAppliEA.param (uncomment the lines youwant to become active), and run               % eoAppliEA @eoAppliEA.param(see the Lesson 5 of the tutorial for more details now).HINTS-----1- If some new classes you create require some user parameter, you caneither read them in the file where they are created (e.g.make_op_Appli.h for variation operators), or pass the eoParser to theconstructor of the class, and read the parameter from the parser.2- If you stick to privacy for the data in your EO class, you willprobably need to write accessors to those data, as well as some publicmethods to modify them, as soon as some other methods need them too.3- The sample make_op_Appli.h supposes that you ony have one crossoverand one mutation operator. However, the code for multiple operators isthere: you can have for instance 2 crossover operators, and chooseamong them according to relative weights (proportional choice) - samefor mutation. Look at the operator section in eoAppliEA.cpp Inparticular, the user parameters cross1Rate and mut1Rate are totallyuseless for a single operator.To add another operator, you have to create another class by mimickingwhat has been done for the first operator.For instance, let's suppose you want to create another mutation.* duplicate the code for eoAppliMutation class * in the second version, change the class name (eoAppliMutation) intoanother name (let's say eoAppliBetterMutation) - you must change thename in the class declaration, in the constructor and in theclassName() method.* in the new eoAppliBetterMutation class, change the code for theoperator() - and eventually the code for the constructor.* in the make_op_Appli.h file, in the mutation section, uncomment the lines       mut = new eoAppliSecondMutation<Indi>(varType  _anyVariable);      _state.storeFunctor(mut);      double mut2Rate = _parser.createParam(1.0, "mut2Rate", "Relative rate for mutation 2", '2', "Variation Operators").value();        propMutation.add(*mut, mut2Rate); and change the name of the class from eoAppliSecondMutation to yourname eoAppliBetterMutation (you can also change the keyword frommut2Rate to something more meaningful like BetterMutationRate).You're done!In case of problem: Marc.Schoenauer@inria.fr