General Purpose Optimization Library (GPOL)

ǂa ǂflexible and efficient multi-purpose optimization library in Python

Illya Bakurov (Avtor), Marco Buzzelli (Avtor), Mauro Castelli (Avtor), Leonardo Vanneschi (Avtor), Raimondo Schettini (Avtor)

Povzetek

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).

Ključne besede

optimization;evolutionary computation;swarm intelligence;local search;continuous optimization;combinatorial optimization;inductive programming;supervised machine learning;

Podatki

Jezik:	Angleški jezik
Leto izida:	2021
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL EF - Ekonomska fakulteta
UDK:	004:78
COBISS:	64772611
ISSN:	2076-3417
Št. ogledov:	292
Št. prenosov:	74
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Angleški jezik
Vrsta dela (COBISS):	Članek v reviji
Strani:	34 str.
Letnik:	ǂVol. ǂ11
Zvezek:	ǂiss. ǂ11, art. 4774
Čas izdaje:	2021
DOI:	10.3390/app11114774
ID:	13002084