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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Ozturk, Elif Goksu, Pedro Rocha, Sousa, F., Lima, Maria Margarida, Rodrigues, Ana Maria, Ferreira, José Soeiro...Oliveira, Cristina Teles (2022). An Application of Preference-Inspired Co-Evolutionary Algorithm to Sectorization. 2nd International Conference Innovation in Engineering, ICIE 2022.
Exportar Referência (IEEE) 
E. G. Ozturk et al.,  "An Application of Preference-Inspired Co-Evolutionary Algorithm to Sectorization", in 2nd Int. Conf. Innovation in Engineering, ICIE 2022, Guimarães, 2022
Exportar BibTeX 
@misc{ozturk2022_1734978104593,
	author = "Ozturk, Elif Goksu and Pedro Rocha and Sousa, F. and Lima, Maria Margarida and Rodrigues, Ana Maria and Ferreira, José Soeiro and Nunes, Ana Catarina and Lopes, Isabel Cristina and Oliveira, Cristina Teles",
	title = "An Application of Preference-Inspired Co-Evolutionary Algorithm to Sectorization",
	year = "2022",
	howpublished = "Ambos (impresso e digital)",
	url = "https://icieng.eu/"
}
Exportar RIS 
TY  - CPAPER
TI  - An Application of Preference-Inspired Co-Evolutionary Algorithm to Sectorization
T2  - 2nd International Conference Innovation in Engineering, ICIE 2022
AU  - Ozturk, Elif Goksu
AU  - Pedro Rocha
AU  - Sousa, F.
AU  - Lima, Maria Margarida
AU  - Rodrigues, Ana Maria
AU  - Ferreira, José Soeiro
AU  - Nunes, Ana Catarina
AU  - Lopes, Isabel Cristina
AU  - Oliveira, Cristina Teles
PY  - 2022
CY  - Guimarães
UR  - https://icieng.eu/
AB  - Sectorization problems have significant challenges arising
from the many objectives that must be optimised simultaneously. Several
methods exist to deal with these many-objective optimisation problems,
but each has its limitations. This paper analyses an application of Preference
Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g)
to sectorization problems. The method is tested on instances of different
size difficulty levels and various configurations for mutation rate and population
number. The main purpose is to find the best configuration for
PICEA-g to solve sectorization problems. Performancemetrics are used to
evaluate these configurations regarding the solutions’ spread, convergence,
and diversity in the solution space. Several test trials showed that big and
medium-sized instances perform better with low mutation rates and large
population sizes. The opposite is valid for the small size instances.
ER  -