This is the old template-directory README. Most of this information is
still 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 to
create 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 of
bitstrings or vector<double>. More details in Lesson5 of the tutorial.

It is assumed in the following that you have read the first part of
the tutorial (Lessons 1 to 4).

Creating the algorithm for your genotype
----------------------------------------
In what follows, we will suppose that you want to evolve some data
structure, and that you have enough programming skills to be able to
write C code for its random initilialization, its crossover, its
mutation and the computation of its fitness.

The helper script * create.sh * will create for you the files you need
from the samples in tutorial/Templates dir, and all you'll have to do
is to include the actual code where indicated in those files (between
keywords 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 file
eoAppliXXX)

1- cd to the tutorial dir

2- create the directory for your application (let's assume you call it
APPLICATION): type in

            mkdir APPLICATION

3- go to the Templates dir 

            cd Templates

and run the helper script create.sh with the following arguments 

            ./create.sh Appli ../APPLICATION

4- 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 operators

Note: You can go directly to step 6 and 7: you'll get a lot of
warnings, but will be able to run an EA that does nothing!

5- Edit those files to suit your needs. The minimal addition you'll need
to 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 genotypes

HINT: look for keywords START and END and modify code in between.

6- Compile eoAppliEA.cpp. If your APPLICATION dir is in the tutorial
dir, you don't need to modify Makefile. Just type in

               % make

7- Run the resulting program:

               % eoAppliEA

The default output is one line per generation with the generation
number, the number of evaluations performed, the best and average
fitnesses in the population.
The algorithm stops by default after 100 generations.

8- Customize the parameters: copy eoAppliEA.status into
e.g. eoAppliEA.param, edit eoAppliEA.param (uncomment the lines you
want 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 can
either read them in the file where they are created (e.g.
make_op_Appli.h for variation operators), or pass the eoParser to the
constructor of the class, and read the parameter from the parser.

2- If you stick to privacy for the data in your EO class, you will
probably need to write accessors to those data, as well as some public
methods 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 crossover
and one mutation operator. However, the code for multiple operators is
there: you can have for instance 2 crossover operators, and choose
among them according to relative weights (proportional choice) - same
for mutation. Look at the operator section in eoAppliEA.cpp In
particular, the user parameters cross1Rate and mut1Rate are totally
useless for a single operator.

To add another operator, you have to create another class by mimicking
what 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) into
another name (let's say eoAppliBetterMutation) - you must change the
name in the class declaration, in the constructor and in the
className() method.
* in the new eoAppliBetterMutation class, change the code for the
operator() - 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 your
name eoAppliBetterMutation (you can also change the keyword from
mut2Rate to something more meaningful like BetterMutationRate).
You're done!

In case of problem: Marc.Schoenauer@inria.fr
