General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Bakurov:2021:AS,
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author = "Illya Bakurov and Marco Buzzelli and
Mauro Castelli and Leonardo Vanneschi and Raimondo Schettini",
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title = "General Purpose Optimization Library ({GPOL}): A
Flexible and Efficient Multi-Purpose Optimization
Library in Python",
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journal = "Applied Sciences",
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year = "2021",
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volume = "11",
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number = "11",
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pages = "Article--number 4774",
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month = "1 " # jun,
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keywords = "genetic algorithms, genetic programming, optimization,
evolutionary computation, swarm intelligence, local
search, continuous optimisation, combinatorial
optimization, inductive programming, supervised machine
learning",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/11/11/4774",
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DOI = "doi:10.3390/app11114774",
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code_url = "https://gitlab.com/ibakurov/general-purpose-optimization-library",
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size = "34 pages",
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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).",
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notes = "Also known as \cite{app11114774}
Nova Information Management School (NOVA IMS),
Universidade NOVA de Lisboa, Campus de
Campolide,1070-312 Lisboa, Portugal",
- }
Genetic Programming entries for
Illya Bakurov
Marco Buzzelli
Mauro Castelli
Leonardo Vanneschi
Raimondo Schettini
Citations