Dagstuhl-Seminar 15211
Theory of Evolutionary Algorithms
( 17. May – 22. May, 2015 )
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Organisatoren
- Benjamin Doerr (Ecole Polytechnique - Palaiseau, FR)
- Nikolaus Hansen (University of Paris South XI, FR)
- Christian Igel (University of Copenhagen, DK)
- Lothar Thiele (ETH Zürich, CH)
Kontakt
- Andreas Dolzmann (für wissenschaftliche Fragen)
- Dagmar Glaser (für administrative Fragen)
Impacts
- Artificial Immune Systems can Beat Evolutionary Algorithms in Combinatorial Optimisation : article : 8 pp - Doerr, Benjamin; Jansen, Thomas; Zarges, Christine - New York : ACM, 2016. - (Proceedings of the 18th Genetic and Evolutionary Computation Conference (GECCO 2016), AIS-BIO : article).
- Example Landscapes to Support Analysis : article in LNCS 9921 - Jansen, Thomas; Zarges, Christine - Berlin : Springer, 2016. - 792-802 pp..
- First Steps Towards a Runtime Comparison of Natural and Artificial Evolution : article in GECCO '15 Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation : pp. 1455-1462 - Paixao, Tiago; Perez Heredia, Jorge; Sudholt, Dirk; Trubenova, Barbora - New York : ACM, 2015. - (GECCO '15 Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation : article).
- How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms : article : pp. 237-274 - Sudholt, Dirk - Cambridge : MIT Press, 2017 - (Evolutionary computation : 25. 2017, 2.
- Projection-Based Restricted Covariance Matrix Adaptation for High Dimension : article in GECCO 2016, Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States - Akimoto, Youhei; Jansen, Nikolaus - HAL Inria, 2016. - 9 pp. - (GECCO 2016, Genetic and Evolutionary Computation Conference 2016 : article.
- Toward a unifying framework for evolutionary processes : article : pp. 28-43 - Paixao, Tiago; Badkobeh, Golnaz; Barton, Nicholas H.; Corus, Dogan; Dang, Duc-Cuong; Trubenova, Barbora; Sutton, Andrew M.; Sudholt, Dirk; Lehre, Per Kristian; Friedrich, Tobias - Amsterdam : Elsevier, 2015 - (Journal of Theoretical Biology ; 383. 2015).
- Update Strength in EDAs and ACO : How to Avoid Genetic Drift : article : 32 pp. - Sudholt, Dirk; Witt, Carsten - Cornell University : arXiv.org, 2016..
- Update Strength in EDAs and ACO : How to Avoid Genetic Drift : article pp. 61-68 - Sudholt, Dirk; Witt, Carsten - New York : ACM, 2016 - (GECCO '16 Proceedings of the Genetic and Evolutionary Computation Conference 2016 ; pp. 61-68).
- Upper bounds on expected hitting time of target subsets by genetic algorithm : article in Proceedings of the 6th International Conference "Optimization Problems and their Economical Applications" : pp.84-88 - Dang, Duc-Cuong; Eremeev, Anton V.; Lehre, Per Kristian - Omsk, 2015. - (Proceedings of the 6th International Conference Optimization Problems and their Economical Applications : article).
Evolutionary algorithms (EAs) are randomized search and optimization methods applicable to problems that are non-continuous, multi-modal, and/or noisy as well as to multi-objective and dynamic optimization tasks. They have successfully been applied to a wide range of real-world applications and demonstrated impressive performance in benchmarks for derivative-free optimization. The goal of this Dagstuhl Seminar is to advance the theory underlying evolutionary algorithms and related methods, in order to gain a better understanding of their properties and to develop new powerful methods in a principled way. The highly international, interdisciplinary seminar will bring together leading experts and young researchers in the field.
The seminar will cover all important streams of research in the theory of evolutionary algorithms with a focus on three topics of current interest:
Rigorous runtime and computational complexity analysis have become the most important tools in the theory of discrete evolutionary computation. The Dagstuhl Seminar series “Theory of Evolutionary Algorithms” has sparked this development, and the drastic increase in new results and young researchers entering this field naturally leads to this focus topic.
Using concepts from information geometry in evolutionary algorithms is one of the most promising new theoretical directions in evolutionary computing and will accordingly be further investigated at the seminar.
Evolutionary computing is rooted in theories of natural evolution, and many early approaches to understand basic properties of evolutionary algorithms were inspired by biological evolution theory. Still, today these two research fields are almost completely separated. We see a great potential for mutual benefit, which we would like to explore by inviting experts from evolution biology.
Evolutionary algorithms (EAs) are randomized search and optimization methods applicable to problems that may be non-continuous, multi-modal, noisy, multi-objective or dynamic. They have successfully been applied to a wide range of real-world applications and have demonstrated impressive performance in benchmarks for derivative-free optimization. The seminar was devoted to the theory underlying evolutionary algorithms and related methods, in order to gain a better understanding of their properties and to develop new powerful methods in a principled way. The highly international, interdisciplinary seminar brought together leading experts and young researchers in the field. The 45 participants came from 13 different countries, spread over 4 continents. Many additional researchers had expressed their interest to also attend the seminar, but could unfortunately not be considered.
Topics
The following report covers all important streams of research in the theory of evolutionary algorithms with a focus on three topics of particular current interest:
Runtime and complexity. Rigorous runtime and analysis and computational complexity theory have become the most important tools in the theory of discrete evolutionary algorithms. The Dagstuhl seminar series ``Theory of Evolutionary Algorithms'' has sparked this development. The drastic increase in new results, new methods, and young researchers entering this field, but also the major unsolved problems naturally lead to keeping this a focus topic.
Information geometry. Using concepts from information geometry in evolutionary algorithms is one of the most promising new theoretical direction in evolutionary computing. The seminar provided a unique opportunity to discuss perspectives and limitations of this approach.
Natural evolution. Evolutionary computing is rooted in theories of natural evolution, and many early approaches to understand basic properties of evolutionary algorithms were inspired by biological evolution theory. Still, today these two research fields are almost completely separated. We invited experts from evolution biology to help better understanding the relations between both fields. We are particularly happy that we succeeded in bringing together researchers from evolution biology and computer science in a way that was stimulating and productive.
Organization
The seminar had three types of organized presentation and discussion formats to stimulate the free discussions among the participants. There were 20--30 minutes talks on current topics followed by discussions. These included a talk on potential industrial collaborations. In addition, we had a few longer talks, which combined recent work with an overview over the state-of-the-art in a certain domain: Thomas Jansen spoke on "Understanding Randomised Search Heuristics", Nick Barton on "Limits to Adaptation", Yann Olivier introduced "Information-geometric Optimization", and Timo Kötzing presented a talk on "Stochastic Fitness Functions and Drift". Furthermore, we continued with having "breakout sessions" for longer, parallel group discussions on timely, specialized topics. These were introduced in the last seminar on "Theory of Evolutionary Algorithms". This time, these session were even more productive than previously, both because the organizers and the participants were more used to this format of interaction. The talks and breakout sessions are summarized in Section~4 of this report.
We would like to thank the Dagstuhl team and the attendees for making seminar 15211 a great success and a pleasure to organize.
Creative Commons BY 3.0 Unported license
Benjamin Doerr, Nikolaus Hansen, Christian Igel, and Lothar Thiele
- Youhei Akimoto (Shinshu University, JP) [dblp]
- Lee Altenberg (Konrad Lorenz Institute for Evolution & Cognitio, AT) [dblp]
- Dirk Arnold (Dalhousie University, CA) [dblp]
- Anne Auger (INRIA Saclay - Île-de-France, FR) [dblp]
- Nick Barton (IST Austria - Klosterneuburg, AT) [dblp]
- Roman Belavkin (Middlesex University - London, GB) [dblp]
- Hans-Georg Beyer (Fachhochschule Vorarlberg, AT) [dblp]
- Dimo Brockhoff (INRIA - University of Lille 1, FR) [dblp]
- Maxim Buzdalov (ITMO University - St. Petersburg, RU) [dblp]
- Arina Buzdalova (ITMO University - St. Petersburg, RU) [dblp]
- Kenneth A. De Jong (George Mason University - Fairfax, US) [dblp]
- Benjamin Doerr (Ecole Polytechnique - Palaiseau, FR) [dblp]
- Carola Doerr (UPMC - Paris, FR) [dblp]
- Anton V. Eremeev (Sobolev Institute of Mathematics - Omsk, RU) [dblp]
- Carlos M. Fonseca (University of Coimbra, PT) [dblp]
- Tobias Friedrich (Hasso-Plattner-Institut - Potsdam, DE) [dblp]
- Tobias Glasmachers (Ruhr-Universität Bochum, DE) [dblp]
- Nikolaus Hansen (University of Paris South XI, FR) [dblp]
- Hsien-Kuei Hwang (Academica Sinica - Taipei, TW) [dblp]
- Christian Igel (University of Copenhagen, DK) [dblp]
- Thomas Jansen (Aberystwyth University, GB) [dblp]
- Daniel Johannsen (SAP Innovation Center - Potsdam, DE) [dblp]
- Joshua D. Knowles (University of Manchester, GB) [dblp]
- Timo Kötzing (Hasso-Plattner-Institut - Potsdam, DE) [dblp]
- Oswin Krause (University of Copenhagen, DK) [dblp]
- Marvin Künnemann (MPI für Informatik - Saarbrücken, DE) [dblp]
- William B. Langdon (University College London, GB) [dblp]
- Per Kristian Lehre (University of Nottingham, GB) [dblp]
- Luigi Malagò (Shinshu University - Nagano, JP & INRIA Saclay, FR) [dblp]
- Silja Meyer-Nieberg (Universität der Bundeswehr - München, DE) [dblp]
- Alberto Moraglio (University of Exeter, GB) [dblp]
- Frank Neumann (University of Adelaide, AU) [dblp]
- Pietro S. Oliveto (University of Sheffield, GB) [dblp]
- Yann Ollivier (University Paris Sud, FR) [dblp]
- Jonathan Rowe (University of Birmingham, GB) [dblp]
- Günter Rudolph (TU Dortmund, DE) [dblp]
- Jonathan L. Shapiro (University of Manchester, GB) [dblp]
- Peter F. Stadler (Universität Leipzig, DE) [dblp]
- Dirk Sudholt (University of Sheffield, GB) [dblp]
- Lothar Thiele (ETH Zürich, CH) [dblp]
- Heike Trautmann (Universität Münster, DE) [dblp]
- Rolf Wanka (Universität Erlangen-Nürnberg, DE) [dblp]
- Carsten Witt (Technical University of Denmark - Lyngby, DK) [dblp]
- Xin Yao (University of Birmingham, GB) [dblp]
- Christine Zarges (University of Birmingham, GB) [dblp]
Verwandte Seminare
- Dagstuhl-Seminar 00071: Theory of Evolutionary Algorithms (2000-02-13 - 2000-02-18) (Details)
- Dagstuhl-Seminar 02031: Theory of Evolutionary Algorithms (2002-01-13 - 2002-01-18) (Details)
- Dagstuhl-Seminar 04081: Theory of Evolutionary Algorithms (2004-02-15 - 2004-02-20) (Details)
- Dagstuhl-Seminar 06061: Theory of Evolutionary Algorithms (2006-02-05 - 2006-02-10) (Details)
- Dagstuhl-Seminar 08051: Theory of Evolutionary Algorithms (2008-01-27 - 2008-02-01) (Details)
- Dagstuhl-Seminar 10361: Theory of Evolutionary Algorithms (2010-09-05 - 2010-09-10) (Details)
- Dagstuhl-Seminar 13271: Theory of Evolutionary Algorithms (2013-06-30 - 2013-07-05) (Details)
- Dagstuhl-Seminar 17191: Theory of Randomized Optimization Heuristics (2017-05-07 - 2017-05-12) (Details)
- Dagstuhl-Seminar 19431: Theory of Randomized Optimization Heuristics (2019-10-20 - 2019-10-25) (Details)
- Dagstuhl-Seminar 22081: Theory of Randomized Optimization Heuristics (2022-02-20 - 2022-02-25) (Details)
- Dagstuhl-Seminar 24271: Theory of Randomized Optimization Heuristics (2024-06-30 - 2024-07-05) (Details)
Klassifikation
- data structures / algorithms / complexity
- optimization / scheduling
- soft computing / evolutionary algorithms
Schlagworte
- evolutionary algorithm
- randomized algorithms
- theoretical computer science
- theoretical biology
