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Authors: Hossam Faris 1 ; Ibrahim Aljarah 1 ; Seyedali Mirjalili 2 ; Pedro A. Castillo 3 and Juan J. Merelo 3

Affiliations: 1 The University of Jordan, Jordan ; 2 Griffith University, Australia ; 3 University of Granada, Spain

Keyword(s): Evolutionary, Swarm Optimization, Metaheuristic, Optimization, Python, Framework.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Memetic Algorithms ; Representation Techniques ; Soft Computing ; Swarm/Collective Intelligence

Abstract: EvoloPy is an open source and cross-platform Python framework that implements a wide range of classical and recent nature-inspired metaheuristic algorithms. The goal of this framework is to facilitate the use of metaheuristic algorithms by non-specialists coming from different domains. With a simple interface and minimal dependencies, it is easier for researchers and practitioners to utilize EvoloPy for optimizing and benchmarking their own defined problems using the most powerful metaheuristic optimizers in the literature. This framework facilitates designing new algorithms or improving, hybridizing and analyzing the current ones. The source code of EvoloPy is publicly available at GitHub (https://github.com/7ossam81/EvoloPy).

CC BY-NC-ND 4.0

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Paper citation in several formats:
Faris, H. ; Aljarah, I. ; Mirjalili, S. ; Castillo, P. and Merelo, J. (2016). EvoloPy: An Open-source Nature-inspired Optimization Framework in Python. In Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016) - ECTA; ISBN 978-989-758-201-1, SciTePress, pages 171-177. DOI: 10.5220/0006048201710177

@conference{ecta16,
author={Hossam Faris and Ibrahim Aljarah and Seyedali Mirjalili and Pedro A. Castillo and Juan J. Merelo},
title={EvoloPy: An Open-source Nature-inspired Optimization Framework in Python},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016) - ECTA},
year={2016},
pages={171-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006048201710177},
isbn={978-989-758-201-1},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016) - ECTA
TI - EvoloPy: An Open-source Nature-inspired Optimization Framework in Python
SN - 978-989-758-201-1
AU - Faris, H.
AU - Aljarah, I.
AU - Mirjalili, S.
AU - Castillo, P.
AU - Merelo, J.
PY - 2016
SP - 171
EP - 177
DO - 10.5220/0006048201710177
PB - SciTePress

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