PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles

Autor: Helen C. Mattos Senefonte, Myriam Regattieri Delgado, Ricardo Luders, Thiago H. Silva

Publikováno v: IEEE Access, Vol 10, Pp 9257-9270 (2022)

This paper proposes PredicTour, an approach to process check-ins made by users of location-based social networks (LBSNs), and predict mobility patterns of tourists visiting new countries with or without previous visiting records. PredicTour is compos

Externí odkaz: https://doaj.org/article/10378291a22b46d2917cccac280b8f31

Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape

Autor: Mohamed El Yafrani, Belaïd Ahiod, Hugo Siqueira, Marcella S. R. Martins, Myriam Regattieri Delgado, Roberto Santana, Ricardo Lüders, Huseyin Gokhan Akcay

Publikováno v: Martins, M S R, El Yafrani, M, Delgado, M, Lüders, R, Santana, R, Siqueira, H V, Akcay, H G & Ahiod, B 2021, ' Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm : a case study on MNK Landscape ', Journal of Heuristics, vol. 27, no. 4, pp. 549-573 . https://doi.org/10.1007/s10732-021-09469-x

This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial prob

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4c17ab08be9ead02ca30f5e78becb71
https://doi.org/10.1007/s10732-021-09469-x

Offline and Online Neural Network Learning in the Context of Smart Homes and Fog Computing

Autor: Lucas V. S. De Mamann, Daniel Fernando Pigatto, Myriam Regattieri Delgado

Publikováno v: Intelligent Systems ISBN: 9783031216886

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0c55829b709e5e46fd4e3b6918253ce3
https://doi.org/10.1007/978-3-031-21689-3_26

Dynamic Learning in Hyper-Heuristics to Solve Flowshop Problems

Autor: Marie-Eléonore Kessaci, Lucas M. Pavelski, Myriam Regattieri Delgado

Publikováno v: BRACIS
BRACIS, Nov 2021, online, Brazil. pp.155-169, ⟨10.1007/978-3-030-91702-9_11⟩
Intelligent Systems ISBN: 9783030917012

Hyper-heuristics (HHs) are algorithms suitable for designing heuristics. HHs perform the search divided in two levels: they look for heuristic components in the high level and the heuristic is used, in the low level, to solve a set of instances of on

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1182857a64b8c0435dffeb6738df3f75
https://hal.science/hal-03509717

A selection hyperheuristic guided by Thompson sampling for numerical optimization

Autor: Ricardo Lüders, Mohamed Abd Elaziz, Myriam Regattieri Delgado, Luiz Ledo, Erick Rodríguez-Esparza, Diego Oliva, Marco Peréz-Cisnero, Mohamed El Yafrani, Marcella Scoczynski

Publikováno v: Scoczynski, M, Oliva, D, Rodríguez-Esparza, E, Delgado, M, Lüders, R, El Yafrani, M, Ledo, L, Elaziz, M A & Peréz-Cisnero, M 2021, A selection hyperheuristic guided by Thompson sampling for numerical optimization . in GECCO 2021 Companion-Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion . Association for Computing Machinery, GECCO 2021 Companion-Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, pp. 1394-1402, 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Virtual, Online, France, 10/07/2021 . https://doi.org/10.1145/3449726.3463140
GECCO Companion

Selection hyper-heuristics have been increasingly and successfully applied to numerical and discrete optimization problems. This paper proposes HHTS, a hyper-heuristic (HH) based on the Thompson Sampling (TS) mechanism to select combinations of low-l

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f681274fcbe0b69a1b1e068252f4cf0b
https://vbn.aau.dk/da/publications/7ef05955-18c1-415d-914f-8d4a2aa239f9