[{"title":"The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems","doi":"10.1007/978-3-319-44003-3_12","date_updated":"2023-10-16T13:34:49Z","publisher":"Springer International Publishing","author":[{"first_name":"Sosa Hernández V","last_name":"Adrián","full_name":"Adrián, Sosa Hernández V"},{"full_name":"Lara, A","last_name":"Lara","first_name":"A"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"full_name":"Rudolph, G","last_name":"Rudolph","first_name":"G"},{"full_name":"Schütze, O","last_name":"Schütze","first_name":"O"}],"date_created":"2023-08-04T15:01:27Z","year":"2017","place":"Cham","citation":{"mla":"Adrián, Sosa Hernández V., et al. “The Directed Search Method for Unconstrained Parameter Dependent Multi-Objective Optimization Problems.” <i>NEO 15</i>, edited by O Schütze et al., Springer International Publishing, 2017, pp. 281–330, doi:<a href=\"https://doi.org/10.1007/978-3-319-44003-3_12\">10.1007/978-3-319-44003-3_12</a>.","short":"S.H.V. Adrián, A. Lara, H. Trautmann, G. Rudolph, O. Schütze, in: O. Schütze, L. Trujillo, P. Legrand, Y. Maldonado (Eds.), NEO 15, Springer International Publishing, Cham, 2017, pp. 281–330.","bibtex":"@inbook{Adrián_Lara_Trautmann_Rudolph_Schütze_2017, place={Cham}, title={The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-44003-3_12\">10.1007/978-3-319-44003-3_12</a>}, booktitle={NEO 15}, publisher={Springer International Publishing}, author={Adrián, Sosa Hernández V and Lara, A and Trautmann, Heike and Rudolph, G and Schütze, O}, editor={Schütze, O and Trujillo, L and Legrand, P and Maldonado, Y}, year={2017}, pages={281–330} }","apa":"Adrián, S. H. V., Lara, A., Trautmann, H., Rudolph, G., &#38; Schütze, O. (2017). The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems. In O. Schütze, L. Trujillo, P. Legrand, &#38; Y. Maldonado (Eds.), <i>NEO 15</i> (pp. 281–330). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-319-44003-3_12\">https://doi.org/10.1007/978-3-319-44003-3_12</a>","ieee":"S. H. V. Adrián, A. Lara, H. Trautmann, G. Rudolph, and O. Schütze, “The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems,” in <i>NEO 15</i>, O. Schütze, L. Trujillo, P. Legrand, and Y. Maldonado, Eds. Cham: Springer International Publishing, 2017, pp. 281–330.","chicago":"Adrián, Sosa Hernández V, A Lara, Heike Trautmann, G Rudolph, and O Schütze. “The Directed Search Method for Unconstrained Parameter Dependent Multi-Objective Optimization Problems.” In <i>NEO 15</i>, edited by O Schütze, L Trujillo, P Legrand, and Y Maldonado, 281–330. Cham: Springer International Publishing, 2017. <a href=\"https://doi.org/10.1007/978-3-319-44003-3_12\">https://doi.org/10.1007/978-3-319-44003-3_12</a>.","ama":"Adrián SHV, Lara A, Trautmann H, Rudolph G, Schütze O. The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems. In: Schütze O, Trujillo L, Legrand P, Maldonado Y, eds. <i>NEO 15</i>. Springer International Publishing; 2017:281–330. doi:<a href=\"https://doi.org/10.1007/978-3-319-44003-3_12\">10.1007/978-3-319-44003-3_12</a>"},"page":"281–330","publication_identifier":{"isbn":["978-3-319-44003-3"]},"language":[{"iso":"eng"}],"_id":"46355","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"abstract":[{"lang":"eng","text":"In this chapter we present the adaptions of the recently proposed Directed Search method to the context of unconstrained parameter dependent multi-objective optimization problems (PMOPs). The new method, called 𝜆-DS, is capable of performing a movement both toward and along the solution set of a given differentiable PMOP. We first discuss the basic variants of the method that use gradient information and describe subsequently modifications that allow for a gradient free realization. Finally, we show that 𝜆-DS can be used to understand the behavior of stochastic local search within PMOPs to a certain extent which might be interesting for the development of future local search engines, or evolutionary strategies, for the treatment of such problems. We underline all our statements with several numerical results indicating the strength of the novel approach."}],"editor":[{"first_name":"O","last_name":"Schütze","full_name":"Schütze, O"},{"last_name":"Trujillo","full_name":"Trujillo, L","first_name":"L"},{"first_name":"P","full_name":"Legrand, P","last_name":"Legrand"},{"first_name":"Y","last_name":"Maldonado","full_name":"Maldonado, Y"}],"status":"public","type":"book_chapter","publication":"NEO 15"}]