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Article

General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python

1
Nova Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide,1070-312 Lisboa, Portugal
2
Dipartimento di Informatica Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca, 336, 20126 Milano, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Peng-Yeng Yin
Appl. Sci. 2021, 11(11), 4774; https://doi.org/10.3390/app11114774
Received: 30 March 2021 / Revised: 6 May 2021 / Accepted: 15 May 2021 / Published: 23 May 2021
(This article belongs to the Special Issue Genetic Programming, Theory, Methods and Applications)
Several interesting libraries for optimization 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. In this paper, 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 optimization 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). View Full-Text
Keywords: optimization; evolutionary computation; swarm intelligence; local search; continuous optimization; combinatorial optimization; inductive programming; supervised machine learning optimization; evolutionary computation; swarm intelligence; local search; continuous optimization; combinatorial optimization; inductive programming; supervised machine learning
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MDPI and ACS Style

Bakurov, I.; Buzzelli, M.; Castelli, M.; Vanneschi, L.; Schettini, R. General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python. Appl. Sci. 2021, 11, 4774. https://doi.org/10.3390/app11114774

AMA Style

Bakurov I, Buzzelli M, Castelli M, Vanneschi L, Schettini R. General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python. Applied Sciences. 2021; 11(11):4774. https://doi.org/10.3390/app11114774

Chicago/Turabian Style

Bakurov, Illya, Marco Buzzelli, Mauro Castelli, Leonardo Vanneschi, and Raimondo Schettini. 2021. "General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python" Applied Sciences 11, no. 11: 4774. https://doi.org/10.3390/app11114774

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