Evoptool: Evolutionary Optimization Tool
Contents
Description
Evolutionary Optimization Tool (Evoptool) is an open source optimization tool writte in C++, distributed under GNU General Public License. Evoptool implements meta-heuristics based on the Evolutionary Computation paradigm and aims to provide a common platform for the development and test of new algorithms, in order to facilitate the performance comparison activity. Evoptool offers a wide set of benchmark problems, from classical toy samples to more complex tasks, and a collection of algorithm implementations from Genetic Algorithms and Estimation of Distribution Algorithms paradigms. Evoptool is flexible, easy to extend, also with algorithms based on other approaches other from EAs.
The Evoptool Team
- Matteo Matteucci - matteucci [AT] elet.polimi.it
- Luigi Malagò - malago [AT] elet.polimi.it
- Gabriele Valentini - gvalentini [AT] iridia.ulb.ac.be
- Davide Cucci - cucci [AT] elet.polimi.it
Formerly involved
- Emanuele Corsano
PPSN 2012
In this section we report the details of the experiments presented in the paper "Variable Transformations in Estimation of Distribution Algorithms", submitted to PPSN 2012.
The experiments were run using the unstable version of evoptool, which is available trought SVN at
https://svn.ws.dei.polimi.it/evoptool/branches/unstable
For each problem, Alternated Bits, Trap3, Trap3 Overlapping and Trep5, a python script is used to generate the Evoptool configuration files. For a given problm size n, we chose a population size, run 24 instances of the test algorithm and check the success ratio. If it below 95%, we increase the population, otherwise we compute from these runs the average number of fitness function evaluations performed before the global optimum appears in the population. We increase the population exponentially with n, so that N=n^k and each time the success ratio is below the chosen we set k=k+0.1.
Each time an experiment is performed using Evoptool, a .tar file is produced which contain statistics and plots for each instance of the algorithms run and also aggregated averages. The python scripts we used name this tar files with the following convention:
algorithm-n-N-selection_rate-maps_number.tar
All the .tar files used to compute the result presented in the paper, along with the python scripts and everything else needed to reproduce the experiments are available trought SVN at
https://svn.ws.dei.polimi.it/evoptool/branches/papers/PPSN-2012-FCA/experiments/
Mailing List
http://groups.google.com/group/evoptool
Features
Algorithms implemented
- Genetic Algorithms
- SGA
- Estimation of Distribution Algorithms
- PBIL, UMDA, cGA, COMIT, BOA, FCA
- DEUM framework
- DEUMd, IsingDEUM, DEUM-Chain, DEUM-LDA, DEUM-CE, DEUM-X2, DEUM-JEMP, DEUM-l1, sDEUM
- Stochastic Gradient Descent
- SGD, SNGD
- Other Meta-heuristics
- Simulated Annealing
References to original papers will be added soon.
Benchmark functions implemented
- OneMax
- AltBit
- Trap3
- Trap5
- IsingSpinGlass2D (*)
- IsingSpinGlass3D (*)
- Max Cut
(*) instances generated and solved with Spin Glass Server. This service is provided by COPhy and M. Jünger's group. http://www.informatik.uni-koeln.de/spinglass/
Download
svn checkout https://svn.ws.dei.polimi.it/evoptool/trunk --username usrevoptool --password evoptoolpsw
Developers may also access unstable branch. Contact the evoptool team for more details.
Installation
Follow these steps in order to compile evoptool
1. Download source code from svn
svn checkout https://svn.ws.dei.polimi.it/evoptool/trunk --username usrevoptool --password evoptoolpsw
Refer to trunk for the lastest (stable?) version of evoptool
2. First you need to manually compile the l1_logreg-0.8.2
package, http://www.stanford.edu/~boyd/l1_logreg/
whose source are already included in the evoptool repository. In order to do that follow the instrunctions in
README.evoptool
in the l1_logreg-0.8.2
directory
Next, compile <id_dist/code> following the instructions in <code>README.evoptool
in the <id_dist
folder
3. Then you need to compile a module at a time. Each module is included in a different folder.
To compile a module, from go to the module source folder and do make lib. For instance, for the module named common
cd common/src make lib cd ..
Compile modules in the following orderm due to dependencies
common functions ga stochastic eda
To clean a module, do
make clean
from the module source directory
4. Go to core
module and type
make exe
Binary files will be copied in the bin
directory in the root as well as the bin
directory of the core
module
Required packages
- Libraries
- gtkmm-2.4
- glademm-2.4
- gthread-2.0
- sigc++-2.0
- opencv (see for instance http://www.samontab.com/web/2011/06/installing-opencv-2-2-in-ubuntu-11-04/)
- gsl
- gslcblas
- libxml++-2.6
- f2c
- Software
- gnuplot
- R (lars package)
Running
Right now evoptool
only runs as a script. GUI is not maintained in the latest version.
The evoptool
binary file is supposed to be run in the bin
directory in the root, this is because right now the script looks for some configuration files, for the instances of the problems, and temp directories. If you want to run the script from other directories, you just need to copy in that directory the evoptool-file
directory that you find in the bin
directory. Link such directory instead of copying it is not safe if you run multiple scripts at the same time, since output files will be shared.
To run the algorithm you need to enter as input an xml file. For instance you can run
./evoptool evoptool-file/examples/unitTestOneMax.xml
The evoptool-file/example
directory contains a set of xml files for different benchmarks and different algorithms.
Take a loot at the example.xml
file for the documentation on hoe to set the parameters for an each execution of evoptool
Each execution of evoptool
produces a set of files as output. You can find all files in evoptool-file/temp
directory. Plus a tar.gz file containing such files can be produced at the end of the execution of evoptool
. See the xml file for details.
evoptool-file/temp/data
contains the raw data of the statistics according to the xml file
evoptool-file/temp/gnuplot
contains the gnuplot files to produce images of the statistics
evoptool-file/temp/image
contains images of the statistics produced according to the xml file
evoptool-file/temp/support
contains logs
Documentation
Implement a new benchmark function
Benchmarks are defined in the function
module. A benchmark is the maximization problem of a real valued function defined over a vector of n binary variables. Functions are maximized in evoptool
. In order to implement a new benchmark function you have to create a new class
that inherits from ObjectiveFunction
in the common
module. Take a look at the OneMax
benchmark function.
Among the arguments of the constructor of the ObjectiveFunction
class there must be the size of the benchmark, which may or may not be a parameter of the new benchmark.
OneMax::OneMax(int size) : ObjectiveFunction(size)
In the constructor you have whether the maximum of the function is known or not
_knownSolution = true;
And in case this is known, set the minimum value and maximum value for the function
_minFitness = 0; _maxFitness = size; //the maximum value of the OneMax function is defined as the sum of the bits of x set to one.
Besides, such values are used in the normalization of the functions in the plots of the statistics.
Each benchmark must implement the f
method, which takes the BinaryString
x and return f(x). For instance, for OneMax
you have
double OneMax::f(BinaryString *bi) { if (bi != NULL) { if(!(bi->validCache())) { /* Fitness cache value not valid */ int sum = 0; for (int j = 0; j < _size; j++) { sum = sum + bi->get(j); } bi->setFitnessCache(sum); return sum; } else { return bi->getFitnessCache(); } } else { cerr << "[OneMax::f] binary instance cannot be null" << endl; return 0; } }
In order to avoid multiple evaluations of the same BinaryString
x, a caching mechanism has been implemented.
Moreover, you are asked to implement the exportInteration
function, which return an HyperGraph
which represents the interactions present in the function. In order to determine such hypergraph, start from the polinomial representation of the function, and for each monomial introduce a new hyperedge in the hypergraph.
For the OneMax
function, such representation corresponds to the independence graph, since such function is linear.
HyperGraph* OneMax::exportInteractions() { HyperGraph *interactions = new HyperGraph(); interactions->createIndependenceGraph(_size); return interactions; }
In Evoptool.h
add an entry in the Tasks
enum.
/* This enumeration defines an identifier for each task function. */ typedef enum { ONE_MAX = 7, } Tasks;
This value will identify uniquely the benchmark in the xml file. In the XMLParser.cpp
file, add an entry for the new function.
bool XMLParser::parseTask(xmlpp::TextReader &reader, Evoptool::TestParameters *params) { .. switch (task) { case Evoptool::ONE_MAX: params->task = new OneMax(size); break; .. default: return false; } .. }
Additional parameters can be obtained using the xml TextReader object, as for the ForPeaks
function.
case Evoptool::FOUR_PEAKS: value = readNodeContent(reader, "peaks", &errorFlag); if (errorFlag) return false; int peaks = toInt(value); params->task = new FourPeaks(size, peaks); break;
Finally in the xml file, a function can set with the task
tag, together with all parameters.
<task> <name>7</name> <size>64</size> </task>
To create a new algorithm
1. Implement a new algorithm
All algorithms must inherit from the Algorithm class. First override the Algorithm::initAlgorithm as follows:
void DEUM::initAlgorithm(ObjectiveFunction* of) { Algorithm::initAlgorithm(of); /* Initialization tasks */ ... }
Then implement a run() method which performs al the required task for one iteration of your algorithm.
There are some classes which contain useful methods such as samplers and estimators from which your algorithm can inherit. The most useful are:
PopulationBasedAlgorithm ModelBasedAlgorithm ExponentialFamilyAlgorithm BayesianNetworkBasedAlgorithm
2. Append entry in the Algorithms enum in Evoptool.h.
typedef enum { ... DEUM // 5 } Algorithms;
In the xml configuration file, your algorithm will be characterized by the int value associated with the position in this enum declaration.
3. modify the file core/src/XMLParser.cpp. Search for the method bool XMLParser::parseAlgorithms and insert an new entry for your algorithm in the algorithmParameters array, for example
AlgorithmParameters algorithmParameters[] = { ... { Evoptool::DEUM, 5, (char*) "IDDDI" }, // Pop, percElitism, percSelec, CoolRate, GibbsIterations ... };
In each entry, the first field is the name of the entry in the Algorithms enum just modified in Evoptool.h, the second is the number of parameters to be parsed in the xml configuration file and the third is a string specifying their type: I for integer and D for double.
4. add a case in the switch that follows in bool XMLParser::parseAlgorithms, for instance:
case Evoptool::DEUM: algos[i] = new extendedFCA(intVal[0], doubleVal[0], doubleVal[1], intVal[1], intVal[2], params->rng); algos[i]->initAlgorithm(params->task); break;
Note that your algorithm can employ the random number generator provided by the Evoptool core, params->rng.
5. Include your headers in core/src/Algorithms.h
6. Document how your algorithm is instantiated in the comments appearing in evoptool-file/exec-scripts/example.xml
Algorithm Id Types Parameters ===================================================================================================== ... FCA 26 "IDDII" (Pop, PercSelection, LearningRate, MaxCompositionLenght, MaxMonomialOrder) ...
7. Add an instantiation example for your algorithm in evoptool-file/exec-scripts/example.xml
<algo id="26" instanceName="FCA"> <int>1000</int> <double>0.5</double> <double>1</double> <int>64</int> <int>2</int> </algo>
Run an algorithm in a C++ program
The simplest way to do this is to create a new submodule in core
, i.e., a new source directory within the source directory of a module
For instance, the steps to create a new submodule named myexample
are
1. copy the example submodule exampleRunAlgorithm
in a new folder named myexample
in
evoptool/core/src
2. modify the Makefile
in the myexample
directory, see comments in the makefile for more details.
3. edit the main
function in the source file, if you need you can create other C++ and .h files in the same folder.
4. from evoptool/core/src/myexample
run
make bin
The bin file is create in evoptool/core/bin
and copied in evoptool/bin
NOTICE that evoptool/core/bin
may be deleted after a make cleanall
5. from evoptool/bin
run
./myexample
Available statistics
In the configuration xml file you can enable the evaluation of three set of statistics:
<statisticsConfiguration> <evalPopulationStatistics>yes</evalPopulationStatistics> <evalModelStatistics>yes</evalModelStatistics> <evalExecutionStatistics>No</evalExecutionStatistics> ... </statisticsConfiguration>
See the headers file in evoptool/common/src for details.