A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems

Autor: Lourdes Uribe, Johan M Bogoya, Andrés Vargas, Adriana Lara, Günter Rudolph, Oliver Schütze

Publikováno v: Mathematics, Vol 8, Iss 10, p 1822 (2020)

Multi-objective optimization problems (MOPs) naturally arise in many applications. Since for such problems one can expect an entire set of optimal solutions, a common task in set based multi-objective optimization is to compute N solutions along the

Externí odkaz: https://doaj.org/article/f505febf83fc43c3922df28230c584d1

Effectiveness of a Structured Group Intervention Based on Pain Neuroscience Education for Patients with Fibromyalgia in Primary Care: a Multicentre Randomized Open-Label Controlled Trial

Autor: Marian Fernández-Luco, Ana J. Romón‐Gómez, Luis Ángel Angón-Puras, Amaia Etxebarria, Eunate Arana‐Arri, Gorka Larrinaga, Maider Azkuenaga, Ainhoa Pikaza, Lourdes Uribe‐Etxebarria, Rafael Gracia-Ballarín, María Jesús Barrenengoa-Cuadra, Ana Zorrilla, María Muñoa‐Capron‐Manieux, Gixane Orrantia

Publikováno v: Addi. Archivo Digital para la Docencia y la Investigación
instname
European Journal of Pain (London, England)
Addi: Archivo Digital para la Docencia y la Investigación
Universidad del País Vasco

Background There has been increased interest in pain neuroscience education (PNE) as a therapeutic approach for the management of fibromyalgia (FM). Methods A multicentre randomized, open-label, controlled trial was conducted to assess the effectiven

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad8e5e9a3dafbf226f9d16e884a6383e
http://hdl.handle.net/10810/51629

Using gradient-free local search within MOEAs for the treatment of constrained MOPs

Autor: Lourdes Uribe, Oliver Schütze, Adriana Lara, Kalyanmoy Deb

Publikováno v: GECCO Companion

Evolutionary algorithms are widely used for the treatment of multi-objective optimization problems due to their global nature, robustness, and their minimal assumptions on the model. In turn, it is widely accepted that they still need quite a few res

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4efecad62cbdc2484cd93b662f260018
https://doi.org/10.1145/3377929.3390028

The Gradient Subspace Approximation and Its Application to Bi-objective Optimization Problems

Autor: Oliver Schütze, Lourdes Uribe, Adriana Lara

Publikováno v: Advances in Dynamics, Optimization and Computation ISBN: 9783030512637

Evolutionary algorithms are very popular and are frequently applied to many different optimization problems. Reasons for this success include that methods of this kind are of global nature, very robust, and only require minimal assumptions on the opt

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d346ba5fd61dcaea9857514d028db36a
https://doi.org/10.1007/978-3-030-51264-4_15

A new gradient free local search mechanism for constrained multi-objective optimization problems

Autor: Oliver Schütze, Lourdes Uribe, Kalyanmoy Deb, Adriana Lara

Publikováno v: Swarm and Evolutionary Computation. 67:100938

Multi-objective evolutionary algorithms (MOEAs) are commonly applied to treat multi-objective optimization problems (MOPs) due to their global nature, robustness, and reliability. However, it is also well known that MOEAs need quite a few resources t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::df7bfdac8f3f13663eaba7a10abd7cd7
https://doi.org/10.1016/j.swevo.2021.100938