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Documents authored by Witt, Carsten

Document
DOI: 10.4230/DagRep.12.5.17
Estimation-of-Distribution Algorithms: Theory and Applications (Dagstuhl Seminar 22182)

Authors: Josu Ceberio Uribe, Benjamin Doerr, Carsten Witt, and Vicente P. Soloviev

Published in: Dagstuhl Reports, Volume 12, Issue 5 (2022)

Abstract
The Dagstuhl seminar 22182 Estimation-of-Distribution Algorithms: Theory and Practice on May 2-6, 2022 brought together 19 international experts in estimation-of-distribution algorithms (EDAs). Their research ranged from a theoretical perspective, e.g., runtime analysis on synthetic problems, to an applied perspective, e.g., solutions of industrial optimization problems with EDAs. This report documents the program and the outcomes of the seminar.
Cite as

Josu Ceberio Uribe, Benjamin Doerr, Carsten Witt, and Vicente P. Soloviev. Estimation-of-Distribution Algorithms: Theory and Applications (Dagstuhl Seminar 22182). In Dagstuhl Reports, Volume 12, Issue 5, pp. 17-36, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{uribe_et_al:DagRep.12.5.17,
  author =	{Uribe, Josu Ceberio and Doerr, Benjamin and Witt, Carsten and Soloviev, Vicente P.},
  title =	{{Estimation-of-Distribution Algorithms: Theory and Applications (Dagstuhl Seminar 22182)}},
  pages =	{17--36},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{5},
  editor =	{Uribe, Josu Ceberio and Doerr, Benjamin and Witt, Carsten and Soloviev, Vicente P.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.5.17},
  URN =		{urn:nbn:de:0030-drops-174421},
  doi =		{10.4230/DagRep.12.5.17},
  annote =	{Keywords: estimation-of-distribution algorithms, heuristic search and optimization, machine learning, probabilistic model building}
}
Document
DOI: 10.4230/LIPIcs.STACS.2012.420
Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation

Authors: Carsten Witt

Published in: LIPIcs, Volume 14, 29th International Symposium on Theoretical Aspects of Computer Science (STACS 2012)

Abstract
The analysis of randomized search heuristics on classes of functions is fundamental for the understanding of the underlying stochastic process and the development of suitable proof techniques. Recently, remarkable progress has been made in bounding the expected optimization time of the simple (1+1) EA on the class of linear functions. We improve the best known bound in this setting from (1.39+o(1))(en ln n) to (en ln n)+O(n) in expectation and with high probability, which is tight up to lower-order terms. Moreover, upper and lower bounds for arbitrary mutations probabilities p are derived, which imply expected polynomial optimization time as long as p=O((ln n)/n) and which are tight if p=c/n for a constant c. As a consequence, the standard mutation probability p=1/n is optimal for all linear functions, and the (1+1) EA is found to be an optimal mutation-based algorithm. Furthermore, the algorithm turns out to be surprisingly robust since large neighborhood explored by the mutation operator does not disrupt the search.
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Carsten Witt. Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation. In 29th International Symposium on Theoretical Aspects of Computer Science (STACS 2012). Leibniz International Proceedings in Informatics (LIPIcs), Volume 14, pp. 420-431, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{witt:LIPIcs.STACS.2012.420,
  author =	{Witt, Carsten},
  title =	{{Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation}},
  booktitle =	{29th International Symposium on Theoretical Aspects of Computer Science (STACS 2012)},
  pages =	{420--431},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-35-4},
  ISSN =	{1868-8969},
  year =	{2012},
  volume =	{14},
  editor =	{D\"{u}rr, Christoph and Wilke, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2012.420},
  URN =		{urn:nbn:de:0030-drops-33920},
  doi =		{10.4230/LIPIcs.STACS.2012.420},
  annote =	{Keywords: Randomized Search Heuristics, Evolutionary Algorithms, Linear Functions, Running Time Analysis}
}
Document
DOI: 10.4230/DagSemProc.10361.1
10361 Abstracts Collection and Executive Summary – Theory of Evolutionary Algorithms

Authors: Anne Auger, Jonathan L. Shapiro, L. Darrell Whitley, and Carsten Witt

Published in: Dagstuhl Seminar Proceedings, Volume 10361, Theory of Evolutionary Algorithms (2010)

Abstract
From September 5 to 10, the Dagstuhl Seminar 10361 ``Theory of Evolutionary Algorithms '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general.
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Anne Auger, Jonathan L. Shapiro, L. Darrell Whitley, and Carsten Witt. 10361 Abstracts Collection and Executive Summary – Theory of Evolutionary Algorithms. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 10361, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{auger_et_al:DagSemProc.10361.1,
  author =	{Auger, Anne and Shapiro, Jonathan L. and Whitley, L. Darrell and Witt, Carsten},
  title =	{{10361 Abstracts Collection and Executive Summary – Theory of Evolutionary Algorithms}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--19},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10361},
  editor =	{Anne Auger and Jonathan L. Shapiro and L. Darrell Whitley and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.10361.1},
  URN =		{urn:nbn:de:0030-drops-28180},
  doi =		{10.4230/DagSemProc.10361.1},
  annote =	{Keywords: Evolutionary algorithms, bio-inspired search heuristics, theoretical analysis, optimization time}
}
Document
DOI: 10.4230/DagSemProc.08051.1
08051 Abstracts Collection – Theory of Evolutionary Algorithms

Authors: Dirk V. Arnold, Anne Auger, Carsten Witt, and Jonathan E. Rowe

Published in: Dagstuhl Seminar Proceedings, Volume 8051, Theory of Evolutionary Algorithms (2008)

Abstract
From Jan. 27, 2008 to Feb. 1, 2008, the Dagstuhl Seminar 08051 ``Theory of Evolutionary Algorithms'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
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Dirk V. Arnold, Anne Auger, Carsten Witt, and Jonathan E. Rowe. 08051 Abstracts Collection – Theory of Evolutionary Algorithms. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 8051, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{arnold_et_al:DagSemProc.08051.1,
  author =	{Arnold, Dirk V. and Auger, Anne and Witt, Carsten and Rowe, Jonathan E.},
  title =	{{08051 Abstracts Collection – Theory of Evolutionary Algorithms}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8051},
  editor =	{Dirk V. Arnold and Anne Auger and Jonathan E. Rowe and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08051.1},
  URN =		{urn:nbn:de:0030-drops-15242},
  doi =		{10.4230/DagSemProc.08051.1},
  annote =	{Keywords: Evolutionary Computation, Theory of Evolutionary Algorithms}
}
Document
DOI: 10.4230/DagSemProc.08051.2
08051 Executive Summary – Theory of Evolutionary Algorithms

Authors: Dirk V. Arnold, Anne Auger, Jonathan E. Rowe, and Carsten Witt

Published in: Dagstuhl Seminar Proceedings, Volume 8051, Theory of Evolutionary Algorithms (2008)

Abstract
The 2008 Dagstuhl Seminar "Theory of Evolutionary Algorithms" was the fifth in a firmly established series of biannual events. In the week from Jan. 27, 2008 to Feb. 1, 2008, 47 researchers from nine countries discussed their recent work and trends in evolutionary computation.
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Dirk V. Arnold, Anne Auger, Jonathan E. Rowe, and Carsten Witt. 08051 Executive Summary – Theory of Evolutionary Algorithms. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 8051, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{arnold_et_al:DagSemProc.08051.2,
  author =	{Arnold, Dirk V. and Auger, Anne and Rowe, Jonathan E. and Witt, Carsten},
  title =	{{08051 Executive Summary – Theory of Evolutionary Algorithms}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8051},
  editor =	{Dirk V. Arnold and Anne Auger and Jonathan E. Rowe and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08051.2},
  URN =		{urn:nbn:de:0030-drops-14812},
  doi =		{10.4230/DagSemProc.08051.2},
  annote =	{Keywords: Evolutionary Algorithms, Theory of Evolutionary Algorithms}
}
Document
DOI: 10.4230/DagSemProc.08051.6
Runtime Analysis of Binary PSO

Authors: Dirk Sudholt and Carsten Witt

Published in: Dagstuhl Seminar Proceedings, Volume 8051, Theory of Evolutionary Algorithms (2008)

Abstract
We investigate the runtime of the Binary Particle Swarm Optimization (PSO) algorithm introduced by Kennedy and Eberhart (1997). The Binary PSO maintains a global best solution and a swarm of particles. Each particle consists of a current position, an own best position and a velocity vector used in a probabilistic process to update the particle's position. We present lower bounds for a broad class of implementations with swarms of polynomial size. To prove upper bounds, we transfer a fitness-level argument well-established for evolutionary algorithms (EAs) to PSO. This method is then applied to estimate the expected runtime on the class of unimodal functions. A simple variant of the Binary PSO is considered in more detail. The1-PSO only maintains one particle, hence own best and global best solutions coincide. Despite its simplicity, the 1-PSO is surprisingly efficient. A detailed analysis for the function Onemax shows that the 1-PSO is competitive to EAs.
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Dirk Sudholt and Carsten Witt. Runtime Analysis of Binary PSO. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 8051, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{sudholt_et_al:DagSemProc.08051.6,
  author =	{Sudholt, Dirk and Witt, Carsten},
  title =	{{Runtime Analysis of Binary PSO}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--22},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8051},
  editor =	{Dirk V. Arnold and Anne Auger and Jonathan E. Rowe and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08051.6},
  URN =		{urn:nbn:de:0030-drops-14800},
  doi =		{10.4230/DagSemProc.08051.6},
  annote =	{Keywords: Particle swarm optimization, runtime analysis}
}
Document
DOI: 10.4230/DagSemProc.06061.8
Runtime Analysis of a Simple Ant Colony Optimization Algorithm

Authors: Frank Neumann and Carsten Witt

Published in: Dagstuhl Seminar Proceedings, Volume 6061, Theory of Evolutionary Algorithms (2006)

Abstract
Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical foundation of this randomized search heuristic is rather weak. Building up such a theory is demanded to understand how these heuristics work as well as to come up with better algorithms for certain problems. Up to now, only convergence results have been achieved showing that optimal solutions can be obtained in a finite amount of time. We present the first runtime analysis of a simple ACO algorithm that transfers many rigorous results with respect to the expected runtime of a simple evolutionary algorithm to our algorithm. In addition, we examine the choice of the evaporation factor, which is a crucial parameter in such an algorithm, in greater detail and analyze its effect with respect to the runtime.
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Frank Neumann and Carsten Witt. Runtime Analysis of a Simple Ant Colony Optimization Algorithm. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 6061, pp. 1-17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{neumann_et_al:DagSemProc.06061.8,
  author =	{Neumann, Frank and Witt, Carsten},
  title =	{{Runtime Analysis of  a Simple Ant Colony Optimization Algorithm}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--17},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6061},
  editor =	{Dirk V. Arnold and Thomas Jansen and Michael D. Vose and Jonathan E. Rowe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06061.8},
  URN =		{urn:nbn:de:0030-drops-5928},
  doi =		{10.4230/DagSemProc.06061.8},
  annote =	{Keywords: Randomized Search Heuristics, Ant Colony Optimization, Runtime Analysis}
}
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