EDAspy: An extensible python package for estimation of distribution algorithms
Estimation of distribution algorithms (EDAs) are a type of evolutionary algorithms where a probabilistic model is learned and sampled in each iteration. EDAspy provides different state-of-the-art implementations of EDAs including the recent semiparametric EDA. The implementations are modularly built...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 598; S. 128043 |
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Elsevier B.V
14.09.2024
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| Abstract | Estimation of distribution algorithms (EDAs) are a type of evolutionary algorithms where a probabilistic model is learned and sampled in each iteration. EDAspy provides different state-of-the-art implementations of EDAs including the recent semiparametric EDA. The implementations are modularly built, allowing for easy extension and the selection of different alternatives, as well as interoperability with new components. EDAspy is totally free and open-source under the MIT license. |
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| AbstractList | Estimation of distribution algorithms (EDAs) are a type of evolutionary algorithms where a probabilistic model is learned and sampled in each iteration. EDAspy provides different state-of-the-art implementations of EDAs including the recent semiparametric EDA. The implementations are modularly built, allowing for easy extension and the selection of different alternatives, as well as interoperability with new components. EDAspy is totally free and open-source under the MIT license. |
| ArticleNumber | 128043 |
| Author | Bielza, Concha Larrañaga, Pedro Soloviev, Vicente P. |
| Author_xml | – sequence: 1 givenname: Vicente P. orcidid: 0000-0003-0050-0235 surname: Soloviev fullname: Soloviev, Vicente P. email: vicente.perez.soloviev@alumnos.upm.es – sequence: 2 givenname: Pedro surname: Larrañaga fullname: Larrañaga, Pedro – sequence: 3 givenname: Concha surname: Bielza fullname: Bielza, Concha |
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| Cites_doi | 10.1080/00401706.2000.10485979 10.18637/jss.v035.i07 10.1038/s41586-020-2649-2 10.1145/3520304.3533963 10.1186/1756-0381-1-6 10.1145/3040718.3040724 10.1007/s10710-019-09367-z 10.1016/j.neucom.2022.06.112 10.1007/s10878-022-00879-6 |
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| Keywords | Benchmarking Bayesian network Evolutionary algorithm Estimation of distribution algorithm |
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