Applying some EDAs and hybrid variants to the ERM problem under uncertainty-Reference-Cited by-同舟云学术

Applying some EDAs and hybrid variants to the ERM problem under uncertainty

Published:2020-07-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
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Author:

Martínez-López Yoan¹,Rodríguez-González Ansel Y.²,Quintana Julio Madera¹,Mayedo Miguel Bethencourt¹,Moya Alexis³,Santiago Oscar Martínez¹

Affiliation:

1. Universidad del Camagüey, Camagüey, Camagüey, Cuba

2. CICESE-UT3, Tepic, Nayarit, México

3. Camagüey University, Camagüey, Camagüey, Cuba

Publisher

ACM

Reference12 articles.

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2. Nikolaus Hansen Sibylle D Müller and Petros Koumoutsakos. 2003. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary computation 11 1 (2003) 1--18. Nikolaus Hansen Sibylle D Müller and Petros Koumoutsakos. 2003. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary computation 11 1 (2003) 1--18.

3. Pierre Hansen and Nenad Mladenović. 2018. Variable Neighborhood Search. Springer International Publishing Cham 759--787. Pierre Hansen and Nenad Mladenović. 2018. Variable Neighborhood Search. Springer International Publishing Cham 759--787.

4. P Larranaga R Etxeberria JA Lozano JM Pena JM Pe etal 1999. Optimization by learning and simulation of Bayesian and Gaussian networks. (1999). P Larranaga R Etxeberria JA Lozano JM Pena JM Pe et al. 1999. Optimization by learning and simulation of Bayesian and Gaussian networks. (1999).

5. Differential evolution strategies for large-scale energy resource management in smart grids

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