License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.7.5.22
URN: urn:nbn:de:0030-drops-82797
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/8279/
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Doerr, Carola ; Igel, Christian ; Thiele, Lothar ; Yao, Xin
Weitere Beteiligte (Hrsg. etc.): Carola Doerr and Christian Igel and Lothar Thiele and Xin Yao

Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)

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dagrep_v007_i005_p022_17191.pdf (3 MB)

Abstract

This report summarizes the talks, breakout sessions, and discussions at the Dagstuhl Seminar 17191 on "Theory of Randomized Optimization Heuristics", held during the week from May 08 until May 12, 2017, in Schloss Dagstuhl -- Leibniz Center for Informatics. The meeting is the successor of the "Theory of Evolutionary Algorithm" seminar series, where the change in the title reflects the development of the research field toward a broader range of heuristics. The seminar has hosted 40 researchers from 15 countries. Topics that have been intensively discussed at the seminar include population-based heuristics, constrained optimization, non-static parameter choices as well as connections to research in machine learning.

BibTeX - Entry

@Article{doerr_et_al:DR:2017:8279,
  author =	{Carola Doerr and Christian Igel and Lothar Thiele and Xin Yao},
  title =	{{Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)}},
  pages =	{22--55},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{7},
  number =	{5},
  editor =	{Carola Doerr and Christian Igel and Lothar Thiele and Xin Yao},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/8279},
  URN =		{urn:nbn:de:0030-drops-82797},
  doi =		{10.4230/DagRep.7.5.22},
  annote =	{Keywords: algorithms and complexity, evolutionary algorithms, machine learning, optimization, soft computing}
}

Keywords: algorithms and complexity, evolutionary algorithms, machine learning, optimization, soft computing
Collection: Dagstuhl Reports, Volume 7, Issue 5
Issue Date: 2017
Date of publication: 20.12.2017

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