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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)
Öztürk, E., Rocha, P., Sousa, F., Lima, M., Rodrigues, A. M., Ferreira, J. S....Oliveira, C. (2022). An application of Preference-Inspired Co-Evolutionary Algorithm to sectorization. In Machado, J., Soares, F., Trojanowska, J., Yildirim, S., Vojtěšek, J., Rea, P., Gramescu, B., and Hrybiuk, O. O. (Ed.), Innovations in Mechatronics Engineering II. Lecture Notes in Mechanical Engineering. (pp. 257-268). Guimarães: Springer.
Exportar Referência (IEEE)
E. G. Ozturk et al.,  "An application of Preference-Inspired Co-Evolutionary Algorithm to sectorization", in Innovations in Mechatronics Engineering II. Lecture Notes in Mechanical Engineering, Machado, J., Soares, F., Trojanowska, J., Yildirim, S., Vojtěšek, J., Rea, P., Gramescu, B., and Hrybiuk, O. O., Ed., Guimarães, Springer, 2022, pp. 257-268
Exportar BibTeX
@inproceedings{ozturk2022_1708548427176,
	author = "Öztürk, E. and Rocha, P. and Sousa, F. and Lima, M. and Rodrigues, A. M. and Ferreira, J. S. and Nunes, A. C. and Lopes, C. and Oliveira, C.",
	title = "An application of Preference-Inspired Co-Evolutionary Algorithm to sectorization",
	booktitle = "Innovations in Mechatronics Engineering II. Lecture Notes in Mechanical Engineering",
	year = "2022",
	editor = "Machado, J., Soares, F., Trojanowska, J., Yildirim, S., Vojtěšek, J., Rea, P., Gramescu, B., and Hrybiuk, O. O.",
	volume = "",
	number = "",
	series = "",
	doi = "10.1007/978-3-031-09385-2_23",
	pages = "257-268",
	publisher = "Springer",
	address = "Guimarães",
	organization = "University of Minho",
	url = "https://link.springer.com/book/10.1007/978-3-031-09385-2"
}
Exportar RIS
TY  - CPAPER
TI  - An application of Preference-Inspired Co-Evolutionary Algorithm to sectorization
T2  - Innovations in Mechatronics Engineering II. Lecture Notes in Mechanical Engineering
AU  - Öztürk, E.
AU  - Rocha, P.
AU  - Sousa, F.
AU  - Lima, M.
AU  - Rodrigues, A. M.
AU  - Ferreira, J. S.
AU  - Nunes, A. C.
AU  - Lopes, C.
AU  - Oliveira, C.
PY  - 2022
SP  - 257-268
SN  - 2195-4356
DO  - 10.1007/978-3-031-09385-2_23
CY  - Guimarães
UR  - https://link.springer.com/book/10.1007/978-3-031-09385-2
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  -