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PermutationGOMEA
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Description
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Source code for the first Gene-pool 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 Gene-pool Optimal Mixing Evolutionary Algorithms.
In T. Friedrich et al., editors,
Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2016,
pages 637-644,
ACM Press,
New York, New York,
2016.
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[Nominated for Best Paper Award]
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The zip file contains 1 version of Permutation GOMEA, written in
C.
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Download
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PermutationGOMEA.zip
[ Current number of downloads:
970 ]
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(MO-)RV-GOMEA
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Description
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Source code for the first Gene-pool Optimal Mixing Evolutionary
Algorithm (GOMEA) instance dedicated to Real-Valued optimization
(RV-GOMEA), both single-objective and multi-objective. The
publications most closely related with this code are:
A. Bouter, T. Alderliesten, C. Witteveen, and P.A.N. Bosman.
Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm.
In G. Ochoa et al.,
Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2017,
pages 705-712,
ACM Press,
New York, New York,
2017.
A. Bouter, N.H. Luong, C. Witteveen, T. Alderliesten, and P.A.N. Bosman.
The multi-objective real-valued gene-pool optimal mixing evolutionary algorithm.
In G. Ochoa et al.,
Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2017,
pages 537-544,
ACM Press,
New York, New York,
2017.
The zip file contains 2 versions of RV-GOMEA (single-objective
and multi-objective), written in C.
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Download
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RV-GOMEA.zip
[ Current number of downloads:
1329 ]
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GP-GOMEA
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Description
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Source code for the first Gene-pool Optimal Mixing Evolutionary
Algorithm (GOMEA) instance dedicated to Genetic Programming
(GP-GOMEA). The publication most closely related with this code
is:
M. Virgolin, T. Alderliesten, C. Witteveen, and P.A.N. Bosman.
Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building block learning.
In G. Ochoa et al.,
Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2017,
pages 1041-1048,
ACM Press,
New York, New York,
2017.
The zip file contains 1 version of GP-GOMEA, written in C++.
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Download
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GP-GOMEA.zip
[ Current number of downloads:
868 ]
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MO-GOMEA
LTGA
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Description
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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 up-to-date.
Source code for the Gene-pool 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 - GECCO-2013,
pages 359-366,
ACM Press,
New York, New York,
2013.
The zip file contains 1 version of LTGA, written in C.
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Download
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LTGA.zip
[ Current number of downloads:
1370 ]
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AMaLGaM
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Description
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Source code for the Estimation-of-Distribution Algorithm (EDA)
known as the Adapted Maximum-Likelihood Gaussian Model Iterated
Density Estimation Evolutionary Algorithm (AMaLGaM-IDEA, or
AMaLGaM for short). The publication most closely related with
this code is:
P.A.N. Bosman, J. Grahl and D. Thierens.
Benchmarking Parameter-free AMaLGaM on Functions With and Without Noise .
In Evolutionary Computation 21(3),
pages 445-469,
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 parameter-free version
of each EDA with an automated restart mechanism. The source code
is written in C.
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Download
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AMaLGaM.zip
[ Current number of downloads:
2299 ]
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