* EO.tpl, MyStructEA.cpp, MyStructSEA.cpp, make_MyStruct.cpp: Use
correct names for includes. * README.manual: This is a copy of the old README. * README: Describe the new way and setup of creating a new EO project. * createEOproject.sh, Makefile.am.src-tmpl, Makefile.am.top-tmpl: * configure.ac.tmpl: New files to create a standalone EO project from templates.
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Quick NOTE: This version of README is obsolete (May 25, 2004)
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In particular, a simpler version of the algorithm can be generated
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using the script
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createSimple
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This Templates directory contains template files of an EO project and
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a script createEOproject.sh to create a complete new EO project.
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with the same syntax. It is also more powerful, allowing for instance
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to create you own statistics on the population, saving it in a file
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and/or plotting on on-line during the run (see eoStat.tmpl).
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More details some day, when I have some time ...
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The template requires a complete installation of EO.
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============= Old README (most is still accurate, though) ==========
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In particular, the C++ compiler must find the EO include files and the
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linker must find the EO libraries. Most probably, that means that you
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have to set the variables CPLUS_INCLUDE_PATH and LIBRARY_PATH, i.e.
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for a standard installation and using tcsh:
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This directory contains sample files that should make it easy to
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create an EO algorithm to evolve any type of structure
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(EO comes with two examples, bitstrings and vector of real variables,
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so you'll need this as soon as you want to evolve something else).
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setenv CPLUS_INCLUDE_PATH /usr/local/include/eo:"$CPLUS_INCLUDE_PATH"
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setenv LIBRARY_PATH /usr/local/lib:"$LIBRARY_PATH"
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At the moment, only algorithms involving a scalar fitness (double)
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are implemented (see test dir for Pareto optimization of multiple-
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objective fitness - or be patient :-)
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This file will help you to build the same algorithm than the ones
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in the Lesson4 of the tutorial, but with YOUR genotype instead of
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bitstrings or vector<double>. More details in Lesson5 of the tutorial.
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It is assumed in the following that you have read the first part of
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the tutorial (Lessons 1 to 4).
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Creating the algorithm for your genotype
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----------------------------------------
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In what follows, we will suppose that you want to evolve some data
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structure, and that you have enough programming skills to be able to
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write C code for its random initilialization, its crossover, its
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mutation and the computation of its fitness.
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The helper script * create.sh * will create for you the files you need
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from the samples in tutorial/Templates dir, and all you'll have to do
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is to include the actual code where indicated in those files (between
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keywords START and END).
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First, let's choose a name: let's call the new EO class eoAppli.
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All newly created classes will be named eoAppliXXX (in the file
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eoAppliXXX)
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1- cd to the tutorial dir
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2- create the directory for your application (let's assume you call it
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APPLICATION): type in
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mkdir APPLICATION
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3- go to the Templates dir
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cd Templates
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and run the helper script create.sh with the following arguments
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./create.sh Appli ../APPLICATION
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4- cd to the APPLICATION dir (cd ../APPLICATION).
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You should see there the following files:
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AppliEA.cpp the main file, includes all other, to be compiled
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Makefile with default target eoAppliEA
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eoAppli.h class eoAppli<FitT>, FitT = template fitness
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eoAppliEvalFunc.h class for the computation of fotness
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eoAppliInit.h class for genotype initlialization
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eoAppliMutation.h class for mutation
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eoAppliQuadCrossover.h class for (quadratic) crossover
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make_genotype_Appli.h helper function that create the initializer
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make_op_Appli.h helper function that creates the variatin operators
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Note: You can go directly to step 6 and 7: you'll get a lot of
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warnings, but will be able to run an EA that does nothing!
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5- Edit those files to suit your needs. The minimal addition you'll need
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to make are
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in eoAppli.h define your genotype
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in eoAppliInit.h define the initialization of one genotype
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in eoAppliMutation.h define the mutation of one genotype
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in eoAppliQuadCrossover.h define the crossover of 2 genotypes
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HINT: look for keywords START and END and modify code in between.
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6- Compile eoAppliEA.cpp. If your APPLICATION dir is in the tutorial
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dir, you don't need to modify Makefile. Just type in
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% make
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7- Run the resulting program:
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% eoAppliEA
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The default output is one line per generation with the generation
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number, the number of evaluations performed, the best and average
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fitnesses in the population.
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The algorithm stops by default after 100 generations.
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8- Customize the parameters: copy eoAppliEA.status into
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e.g. eoAppliEA.param, edit eoAppliEA.param (uncomment the lines you
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want to become active), and run
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% eoAppliEA @eoAppliEA.param
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(see the Lesson 5 of the tutorial for more details now).
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HINTS
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-----
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1- If some new classes you create require some user parameter, you can
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either read them in the file where they are created (e.g.
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make_op_Appli.h for variation operators), or pass the eoParser to the
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constructor of the class, and read the parameter from the parser.
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2- If you stick to privacy for the data in your EO class, you will
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probably need to write accessors to those data, as well as some public
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methods to modify them, as soon as some other methods need them too.
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3- The sample make_op_Appli.h supposes that you ony have one crossover
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and one mutation operator. However, the code for multiple operators is
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there: you can have for instance 2 crossover operators, and choose
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among them according to relative weights (proportional choice) - same
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for mutation. Look at the operator section in eoAppliEA.cpp In
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particular, the user parameters cross1Rate and mut1Rate are totally
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useless for a single operator.
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To add another operator, you have to create another class by mimicking
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what has been done for the first operator.
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For instance, let's suppose you want to create another mutation.
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* duplicate the code for eoAppliMutation class
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* in the second version, change the class name (eoAppliMutation) into
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another name (let's say eoAppliBetterMutation) - you must change the
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name in the class declaration, in the constructor and in the
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className() method.
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* in the new eoAppliBetterMutation class, change the code for the
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operator() - and eventually the code for the constructor.
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* in the make_op_Appli.h file, in the mutation section, uncomment the
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lines
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mut = new eoAppliSecondMutation<Indi>(varType _anyVariable);
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_state.storeFunctor(mut);
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double mut2Rate = _parser.createParam(1.0, "mut2Rate", "Relative rate for mutation 2", '2', "Variation Operators").value();
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propMutation.add(*mut, mut2Rate);
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and change the name of the class from eoAppliSecondMutation to your
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name eoAppliBetterMutation (you can also change the keyword from
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mut2Rate to something more meaningful like BetterMutationRate).
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You're done!
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In case of problem: Marc.Schoenauer@inria.fr
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See README.manual for more details.
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