- author = "Illya Bakurov and Marco Buzzelli and Mauro Castelli and Leonardo Vanneschi and Raimondo Schettini",
- title = "General Purpose Optimization Library ({GPOL}): A Flexible and Efficient Multi-Purpose Optimization Library in Python",
- journal = "Applied Sciences",
- year = "2021",
- volume = "11",
- number = "11",
- pages = "Article--number 4774",
- month = "1 " # jun,
- keywords = "genetic algorithms, genetic programming, optimization, evolutionary computation, swarm intelligence, local search, continuous optimisation, combinatorial optimization, inductive programming, supervised machine learning",
- ISSN = "2076-3417",
-
URL = "
https://www.mdpi.com/2076-3417/11/11/4774",
-
DOI = "
doi:10.3390/app11114774",
-
code_url = "https://gitlab.com/ibakurov/general-purpose-optimization-library",
- size = "34 pages",
- abstract = "Several interesting libraries for optimisation have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimisation and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).",
-
notes = "Also known as \cite{app11114774}
Nova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide,1070-312 Lisboa, Portugal",
