| Name | digneapy JSON |
| Version |
0.2.5
JSON |
| download |
| home_page | https://github.com/DIGNEA/digneapy |
| Summary | Python version of the DIGNEA code for instance generation |
| upload_time | 2024-10-21 11:37:22 |
| maintainer | None |
| docs_url | None |
| author | Alejandro Marrero |
| requires_python | >=3.10 |
| license | GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Python Boilerplate contains all the boilerplate you need to create a Python package. Copyright (C) 2023 Alejandro Marrero This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. Also add information on how to contact you by electronic and paper mail. You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>. |
| keywords |
dignea
optimization
instance generation
quality-diversity
ns
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
keras
pandas
deap
numpy
scikit-learn
scipy
torch
pybind11
seaborn
matplotlib
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# DIGNEApy
---
Diverse Instance Generator with Novelty Search and Evolutionary Algorithms
[](https://github.com/DIGNEA/DIGNEApy/actions/workflows/python-app.yml)
[](https://coveralls.io/github/DIGNEA/DIGNEApy?branch=main)
[](https://www.gnu.org/licenses/gpl-3.0)
Repository containing the Python version of DIGNEA, a Diverse Instance Generator with Novelty Search and Evolutionary Algorithms. This framework is an extensible tool for generating diverse and discriminatory instances for any desired domain. The instances obtained generated will be biased to the performance of a *target* in a specified portfolio of algorithms.
## Dependencies
- Numpy
- Sklearn
- Pandas
- Keras
- DEAP
- PyTorch
- Pybind11
- Seaborn
- Matplotlib
## Publications
DIGNEA was used in the following publications:
* Alejandro Marrero, Eduardo Segredo, and Coromoto Leon. 2021. A parallel genetic algorithm to speed up the resolution of the algorithm selection problem. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, New York, NY, USA, 1978–1981. DOI:https://doi.org/10.1145/3449726.3463160
* Marrero, A., Segredo, E., León, C., Hart, E. 2022. A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13398. Springer, Cham. https://doi.org/10.1007/978-3-031-14714-2_16
* Alejandro Marrero, Eduardo Segredo, Emma Hart, Jakob Bossek, and Aneta Neumann. 2023. Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23). Association for Computing Machinery, New York, NY, USA, 312–320. https://doi.org/10.1145/3583131.3590504
* Marrero, A., Segredo, E., León, C., & Hart, E. 2024. Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. In Genetic and Evolutionary Computation Conference (GECCO ’24), July 14–18, 2024, Melbourne, VIC, Australia. https://doi.org/10.1145/3638529.3654028
* Alejandro Marrero, Eduardo Segredo, Coromoto León, Emma Hart; Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation 2024; doi: https://doi.org/10.1162/evco_a_00350
* Marrero, A. 2024. Evolutionary Computation Methods for Instance Generation in Optimisation Domains. PhD thesis. Universidad de La Laguna. https://riull.ull.es/xmlui/handle/915/37726
## How to cite DIGNEA
If you use DIGNEA in your research work, remember to cite:
>
>@article{dignea_23,
>title = {DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains},
>journal = {SoftwareX},
>volume = {22},
>pages = {101355},
>year = {2023},
>issn = {2352-7110},
>doi = {https://doi.org/10.1016/j.softx.2023.101355},
>url = {https://www.sciencedirect.com/science/article/pii/S2352711023000511},
>author = {Alejandro Marrero and Eduardo Segredo and Coromoto León and Emma Hart},
>keywords = {Instance generation, Novelty search, Evolutionary algorithm, Optimisation, Knapsack problem},
>abstract = {To advance research in the development of optimisation algorithms, it is crucial to have access to large test-beds of diverse and discriminatory instances from a domain that can highlight strengths and weaknesses of different algorithms. The DIGNEA tool enables diverse instance suites to be generated for any domain, that are also discriminatory with respect to a set of solvers of the user choice. Written in C++, and delivered as a repository and as a Docker image, its modular and template-based design enables it to be easily adapted to multiple domains and types of solvers with minimal effort. This paper exemplifies how to generate instances for the Knapsack Problem domain.}
>}
>
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"description": "# DIGNEApy\n---\nDiverse Instance Generator with Novelty Search and Evolutionary Algorithms\n \n[](https://github.com/DIGNEA/DIGNEApy/actions/workflows/python-app.yml)\n[](https://coveralls.io/github/DIGNEA/DIGNEApy?branch=main)\n[](https://www.gnu.org/licenses/gpl-3.0)\n\n\nRepository containing the Python version of DIGNEA, a Diverse Instance Generator with Novelty Search and Evolutionary Algorithms. This framework is an extensible tool for generating diverse and discriminatory instances for any desired domain. The instances obtained generated will be biased to the performance of a *target* in a specified portfolio of algorithms. \n\n\n## Dependencies\n\n- Numpy\n- Sklearn\n- Pandas\n- Keras\n- DEAP \n- PyTorch\n- Pybind11\n- Seaborn\n- Matplotlib\n \n\n## Publications\n\nDIGNEA was used in the following publications:\n\n* Alejandro Marrero, Eduardo Segredo, and Coromoto Leon. 2021. A parallel genetic algorithm to speed up the resolution of the algorithm selection problem. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, New York, NY, USA, 1978\u20131981. DOI:https://doi.org/10.1145/3449726.3463160\n\n* Marrero, A., Segredo, E., Le\u00f3n, C., Hart, E. 2022. A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tu\u0161ar, T. (eds) Parallel Problem Solving from Nature \u2013 PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13398. Springer, Cham. https://doi.org/10.1007/978-3-031-14714-2_16\n\n* Alejandro Marrero, Eduardo Segredo, Emma Hart, Jakob Bossek, and Aneta Neumann. 2023. Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23). Association for Computing Machinery, New York, NY, USA, 312\u2013320. https://doi.org/10.1145/3583131.3590504\n \n* Marrero, A., Segredo, E., Le\u00f3n, C., & Hart, E. 2024. Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. In Genetic and Evolutionary Computation Conference (GECCO \u201924), July 14\u201318, 2024, Melbourne, VIC, Australia. https://doi.org/10.1145/3638529.3654028\n\n* Alejandro Marrero, Eduardo Segredo, Coromoto Le\u00f3n, Emma Hart; Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation 2024; doi: https://doi.org/10.1162/evco_a_00350\n\n* Marrero, A. 2024. Evolutionary Computation Methods for Instance Generation in Optimisation Domains. PhD thesis. Universidad de La Laguna. https://riull.ull.es/xmlui/handle/915/37726\n\n## How to cite DIGNEA\n\nIf you use DIGNEA in your research work, remember to cite: \n\n>\n>@article{dignea_23,\n>title = {DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains},\n>journal = {SoftwareX},\n>volume = {22},\n>pages = {101355},\n>year = {2023},\n>issn = {2352-7110},\n>doi = {https://doi.org/10.1016/j.softx.2023.101355},\n>url = {https://www.sciencedirect.com/science/article/pii/S2352711023000511},\n>author = {Alejandro Marrero and Eduardo Segredo and Coromoto Le\u00f3n and Emma Hart},\n>keywords = {Instance generation, Novelty search, Evolutionary algorithm, Optimisation, Knapsack problem},\n>abstract = {To advance research in the development of optimisation algorithms, it is crucial to have access to large test-beds of diverse and discriminatory instances from a domain that can highlight strengths and weaknesses of different algorithms. The DIGNEA tool enables diverse instance suites to be generated for any domain, that are also discriminatory with respect to a set of solvers of the user choice. Written in C++, and delivered as a repository and as a Docker image, its modular and template-based design enables it to be easily adapted to multiple domains and types of solvers with minimal effort. This paper exemplifies how to generate instances for the Knapsack Problem domain.}\n>}\n>\n",
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