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Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem

Salto, Carolina et al · SEDICI UNLP · 2001

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Evolutionary algorithms (EAs) have been successfully applied to scheduling problems. Current improvements towards convergence issues in EAs include incest prevention and multiplicity features. A multiplicity feature allows multiple recombination on multiple parents [7, 8, 9, 10]. The method was successfully applied to multimodal optimization problems. As a consequence of this approach it was detected that all individuals of the final population are much more centred on the optimum. This is an important issue when the application requires provision of multiple alternative near-optimal solutions confronting system dynamics as in production planning Eje: Inteligencia Computacional - Metaheurísticas Red de Universidades con Carreras en Informática (RedUNCI)

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APA 7

Salto, C. E. A. (2001). Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/21663

MLA

Salto, Carolina et al. Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem. SEDICI UNLP, 2001. http://sedici.unlp.edu.ar/handle/10915/21663.

Chicago

Salto, Carolina et al. 2001. Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/21663.

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Salto, C. E. A. 2001, Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem, SEDICI UNLP, available at: http://sedici.unlp.edu.ar/handle/10915/21663 [Accessed 6 Aug. 2026].

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Title
Incest prevention and multicore combinated evolutionary algorithms for the job shop scheduling problem
Author / contributors
Salto, Carolina et al
Publisher
SEDICI UNLP
Publication year
2001
Language
English

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