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PermutationGOMEA
Description


Source code for the first Genepool Optimal Mixing Evolutionary
Algorithm (GOMEA) instance dedicated to permutation
optimization. The publication most closely related with this
code is:
P.A.N. Bosman, N.H. Luong, and D. Thierens.
Expanding from Discrete Cartesian to Permutation Genepool Optimal Mixing Evolutionary Algorithms.
In T. Friedrich et al., editors,
Proceedings of the Genetic and Evolutionary Computation Conference  GECCO2016,
pages 637644,
ACM Press,
New York, New York,
2016.
[Nominated for Best Paper Award]

The zip file contains 1 version of Permutation GOMEA, written in
C.

Download


PermutationGOMEA.zip
[ Current number of downloads:
179 ]

(MO)RVGOMEA
Description


Source code for the first Genepool Optimal Mixing Evolutionary
Algorithm (GOMEA) instance dedicated to RealValued optimization
(RVGOMEA), both singleobjective and multiobjective. The
publications most closely related with this code are:
A. Bouter, T. Alderliesten, C. Witteveen, and P.A.N. Bosman.
Exploiting linkage information in realvalued optimization with the realvalued genepool optimal mixing evolutionary algorithm.
In G. Ochoa et al.,
Proceedings of the Genetic and Evolutionary Computation Conference  GECCO2017,
pages 705712,
ACM Press,
New York, New York,
2017.
A. Bouter, N.H. Luong, C. Witteveen, T. Alderliesten, and P.A.N. Bosman.
The multiobjective realvalued genepool optimal mixing evolutionary algorithm.
In G. Ochoa et al.,
Proceedings of the Genetic and Evolutionary Computation Conference  GECCO2017,
pages 537544,
ACM Press,
New York, New York,
2017.
The zip file contains 2 versions of RVGOMEA (singleobjective
and multiobjective), written in C.

Download


RVGOMEA.zip
[ Current number of downloads:
326 ]

GPGOMEA
Description


Source code for the first Genepool Optimal Mixing Evolutionary
Algorithm (GOMEA) instance dedicated to Genetic Programming
(GPGOMEA). The publication most closely related with this code
is:
M. Virgolin, T. Alderliesten, C. Witteveen, and P.A.N. Bosman.
Scalable genetic programming by genepool optimal mixing and inputspace entropybased building block learning.
In G. Ochoa et al.,
Proceedings of the Genetic and Evolutionary Computation Conference  GECCO2017,
pages 10411048,
ACM Press,
New York, New York,
2017.
The zip file contains 1 version of GPGOMEA, written in C++.

Download


GPGOMEA.zip
[ Current number of downloads:
263 ]

MOGOMEA
LTGA
Description


NOTE: THIS IS NOT THE RECENTMOST VERSION OF BINARY GOMEA. Code for that version is best obtained by taking PermutationGOMEA and changing the random keys representation into the binary representation as found in LTGA.zip. Sorry for the inconvenience. We are working on a more complete GOMEA platform code that should remain uptodate.
Source code for the Genepool Optimal Mixing Evolutionary
Algorithm (GOMEA) instance that is also known as the Linkage
Tree Genetic Algorithm (LTGA). The publication most closely
related with this code is:
P.A.N. Bosman and D. Thierens.
More Concise and Robust Linkage Learning by Filtering and Combining Linkage Hierarchies.
In C. Blum and E. Alba, editors,
Proceedings of the Genetic and Evolutionary Computation Conference  GECCO2013,
pages 359366,
ACM Press,
New York, New York,
2013.
The zip file contains 1 version of LTGA, written in C.

Download


LTGA.zip
[ Current number of downloads:
702 ]

AMaLGaM
Description


Source code for the EstimationofDistribution Algorithm (EDA)
known as the Adapted MaximumLikelihood Gaussian Model Iterated
Density Estimation Evolutionary Algorithm (AMaLGaMIDEA, or
AMaLGaM for short). The publication most closely related with
this code is:
P.A.N. Bosman, J. Grahl and D. Thierens.
Benchmarking Parameterfree AMaLGaM on Functions With and Without Noise .
In Evolutionary Computation 21(3),
pages 445469,
2013.
The zip file contains 12 versions of AMaLGaM. The Gaussian
distribution is either not factorized, meaning a full covariance
matrix is used, factorized using a Bayesian factorization that
is learned in a greedy fashion, or factorized using the
univariate factorization. For each of these three
factorizations, the distribution can either be learned
incrementally or not. Finally, there is a parameterfree version
of each EDA with an automated restart mechanism. The source code
is written in C.

Download


AMaLGaM.zip
[ Current number of downloads:
1314 ]

