28 Search Results for "Branke, J�rgen"

Document
DOI: 10.4230/OASIcs.SCOR.2012.11
Revenue maximization through dynamic pricing under unknown market behaviour

Authors: Sergio Morales-Enciso and Jürgen Branke

Published in: OASIcs, Volume 22, 3rd Student Conference on Operational Research (2012)

Abstract
We consider the scenario of a multimodal memoryless market to sell one product, where a customer’s probability to actually buy the product depends on the price. We would like to set the price for each customer in a way that maximizes our overall revenue. In this case, an exploration vs. exploitation problem arises. If we explore customer responses to different prices, we get a pretty good idea of what customers are willing to pay. On the other hand, this comes at the cost of losing a customer (when we set the price too high) or selling the product too cheap (when we set the price too low). The goal is to infer the true underlying probability curve as a function of the price (market behaviour) while maximizing the revenue at the same time. This paper focuses on learning the underlying market characteristics with as few data samples as possible by exploiting the knowledge gained from both exploring potentially profitable areas with high uncertainty and optimizing the trade-off between knowledge gained and revenue exploitation. The response variable being binary by nature, classification methods such as logistic regression and Gaussian processes are explored. Two new policies adapted to non parametric inference models are presented, one based on the efficient global optimization (EGO) algorithm and the second based on a dynamic programming approach. Series of simulations of the evolution of the proposed model are finally presented to summarize the achieved performance of the policies.
Cite as

Sergio Morales-Enciso and Jürgen Branke. Revenue maximization through dynamic pricing under unknown market behaviour. In 3rd Student Conference on Operational Research. Open Access Series in Informatics (OASIcs), Volume 22, pp. 11-20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{moralesenciso_et_al:OASIcs.SCOR.2012.11,
  author =	{Morales-Enciso, Sergio and Branke, J\"{u}rgen},
  title =	{{Revenue maximization through dynamic pricing under unknown market behaviour}},
  booktitle =	{3rd Student Conference on Operational Research},
  pages =	{11--20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-39-2},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{22},
  editor =	{Ravizza, Stefan and Holborn, Penny},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SCOR.2012.11},
  URN =		{urn:nbn:de:0030-drops-35426},
  doi =		{10.4230/OASIcs.SCOR.2012.11},
  annote =	{Keywords: Dynamic pricing, revenue management, EGO, Gaussian processes for classification}
}
Document
DOI: 10.4230/DagSemProc.09181.1
09181 Abstracts Collection – Sampling-based Optimization in the Presence of Uncertainty

Authors: Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
This Dagstuhl seminar brought together researchers from statistical ranking and selection; experimental design and response-surface modeling; stochastic programming; approximate dynamic programming; optimal learning; and the design and analysis of computer experiments with the goal of attaining a much better mutual understanding of the commonalities and differences of the various approaches to sampling-based optimization, and to take first steps toward an overarching theory, encompassing many of the topics above.
Cite as

Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner. 09181 Abstracts Collection – Sampling-based Optimization in the Presence of Uncertainty. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{branke_et_al:DagSemProc.09181.1,
  author =	{Branke, J\"{u}rgen and Nelson, Barry L. and Powell, Warren Buckler and Santner, Thomas J.},
  title =	{{09181 Abstracts Collection – Sampling-based Optimization in the Presence of Uncertainty}},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.1},
  URN =		{urn:nbn:de:0030-drops-21187},
  doi =		{10.4230/DagSemProc.09181.1},
  annote =	{Keywords: Optimal learning, optimization in the presence of uncertainty, simulation optimization, sequential experimental design, ranking and selection, random search, stochastic approximation, approximate dynamic programming}
}
Document
DOI: 10.4230/DagSemProc.09181.2
09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty

Authors: Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
This Dagstuhl seminar brought together researchers from statistical ranking and selection; experimental design and response-surface modeling; stochastic programming; approximate dynamic programming; optimal learning; and the design and analysis of computer experiments with the goal of attaining a much better mutual understanding of the commonalities and differences of the various approaches to sampling-based optimization, and to take first steps toward an overarching theory, encompassing many of the topics above.
Cite as

Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner. 09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{branke_et_al:DagSemProc.09181.2,
  author =	{Branke, J\"{u}rgen and Nelson, Barry L. and Powell, Warren Buckler and Santner, Thomas J.},
  title =	{{09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty }},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.2},
  URN =		{urn:nbn:de:0030-drops-21161},
  doi =		{10.4230/DagSemProc.09181.2},
  annote =	{Keywords: Optimal learning, optimization in the presence of uncertainty, simulation optimization, sequential experimental design, ranking and selection, random search, stochastic approximation, approximate dynamic programming}
}
Document
DOI: 10.4230/DagSemProc.09181.3
09181 Working Group on Hybridization between R&S, DoE and Optimization

Authors: Chun-Hung Chen, Liu Hong, Paul B. Kantor, David P. Morton, Juta Pichitlamken, and Matthias Seeger

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
This is the report of the working group on the relation between, or hybrid combination of design experiment optimization and R&S. The rapporteur, Paul Kantor, learned a great deal at the conference which he summarized by sharing the cartoon shown here. ("A student asking the teacher'... may i be excused, my is full" (from a 1986 cartoon by Gary Larson) - omitted here for copyright reasons).
Cite as

Chun-Hung Chen, Liu Hong, Paul B. Kantor, David P. Morton, Juta Pichitlamken, and Matthias Seeger. 09181 Working Group on Hybridization between R&S, DoE and Optimization. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{chen_et_al:DagSemProc.09181.3,
  author =	{Chen, Chun-Hung and Hong, Liu and Kantor, Paul B. and Morton, David P. and Pichitlamken, Juta and Seeger, Matthias},
  title =	{{09181 Working Group on Hybridization between R\&S, DoE and Optimization}},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.3},
  URN =		{urn:nbn:de:0030-drops-21172},
  doi =		{10.4230/DagSemProc.09181.3},
  annote =	{Keywords: }
}
Document
DOI: 10.4230/DagSemProc.09181.4
Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

Authors: Matthias Seeger and Hannes Nickisch

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
Sparsity is a fundamental concept of modern statistics, and often the only general principle available at the moment to address novel learning applications with many more variables than observations. While much progress has been made recently in the theoretical understanding and algorithmics of sparse point estimation, higher-order problems such as covariance estimation or optimal data acquisition are seldomly addressed for sparsity-favouring models, and there are virtually no algorithms for large scale applications of these. We provide novel approximate Bayesian inference algorithms for sparse generalized linear models, that can be used with hundred thousands of variables, and run orders of magnitude faster than previous algorithms in domains where either apply. By analyzing our methods and establishing some novel convexity results, we settle a long-standing open question about variational Bayesian inference for continuous variable models: the Gaussian lower bound relaxation, which has been used previously for a range of models, is proved to be a convex optimization problem, if and only if the posterior mode is found by convex programming. Our algorithms reduce to the same computational primitives than commonly used sparse estimation methods do, but require Gaussian marginal variance estimation as well. We show how the Lanczos algorithm from numerical mathematics can be employed to compute the latter. We are interested in Bayesian experimental design here (which is mainly driven by efficient approximate inference), a powerful framework for optimizing measurement architectures of complex signals, such as natural images. Designs optimized by our Bayesian framework strongly outperform choices advocated by compressed sensing theory, and with our novel algorithms, we can scale it up to full-size images. Immediate applications of our method lie in digital photography and medical imaging. We have applied our framework to problems of magnetic resonance imaging design and reconstruction, and part of this work appeared at a conference (Seeger et al., 2008). The present paper describes our methods in much greater generality, and most of the theory is novel. Experiments and evaluations will be given in a later paper.
Cite as

Matthias Seeger and Hannes Nickisch. Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{seeger_et_al:DagSemProc.09181.4,
  author =	{Seeger, Matthias and Nickisch, Hannes},
  title =	{{Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models}},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.4},
  URN =		{urn:nbn:de:0030-drops-21148},
  doi =		{10.4230/DagSemProc.09181.4},
  annote =	{Keywords: Bayesian experimental design, variational inference, sparse estimation}
}
Document
DOI: 10.4230/DagSemProc.09181.5
Sequential Parameter Optimization

Authors: Thomas Bartz-Beielstein

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
We provide a comprehensive, effective and very efficient methodology for the design and experimental analysis of algorithms. We rely on modern statistical techniques for tuning and understanding algorithms from an experimental perspective. Therefore, we make use of the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems. Two case studies, which illustrate the applicability of SPO to algorithm tuning and model selection, are presented.
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Thomas Bartz-Beielstein. Sequential Parameter Optimization. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{bartzbeielstein:DagSemProc.09181.5,
  author =	{Bartz-Beielstein, Thomas},
  title =	{{Sequential Parameter Optimization}},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--32},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.5},
  URN =		{urn:nbn:de:0030-drops-21159},
  doi =		{10.4230/DagSemProc.09181.5},
  annote =	{Keywords: Optimization, evolutionary algorithms, design of experiments}
}
Document
DOI: 10.4230/DagSemProc.06501.1
06501 Abstracts Collection – Practical Approaches to Multi-Objective Optimization

Authors: Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Roman Slowinski

Published in: Dagstuhl Seminar Proceedings, Volume 6501, Practical Approaches to Multi-Objective Optimization (2007)

Abstract
From 10.12.06 to 15.12.06, the Dagstuhl Seminar 06501 ``Practical Approaches to Multi-Objective Optimization'' 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.
Cite as

Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Roman Slowinski. 06501 Abstracts Collection – Practical Approaches to Multi-Objective Optimization. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 6501, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{branke_et_al:DagSemProc.06501.1,
  author =	{Branke, J\"{u}rgen and Deb, Kalyanmoy and Miettinen, Kaisa and Slowinski, Roman},
  title =	{{06501 Abstracts Collection – Practical Approaches to Multi-Objective Optimization}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{6501},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman Slowinski},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.06501.1},
  URN =		{urn:nbn:de:0030-drops-11224},
  doi =		{10.4230/DagSemProc.06501.1},
  annote =	{Keywords: Multi-criteria optimization, evolutionary and classical methods, interaction}
}
Document
DOI: 10.4230/DagSemProc.06501.2
Reference Point Approaches and Objective Ranking

Authors: Andrzej P. Wierzbicki

Published in: Dagstuhl Seminar Proceedings, Volume 6501, Practical Approaches to Multi-Objective Optimization (2007)

Abstract
The paper presents a reflection on some of the basic assumptions and philosophy of reference point approaches, stressing their unique concentra-tion on the sovereignty of the subjective decision maker. As a new devel-opment in reference point approaches also the concept of objective ranking is stressed, defined as dependent only on a given set of data, relevant for the decision situation, and independent from any more detailed specifica-tion of personal preferences than that given by defining criteria and the partial order in criterion space. Rational objective ranking can be based on reference point approach, because reference levels needed in this approach can be established objectively statistically from the given data set. Exam-ples show that such objective ranking can be very useful in many man-agement situations.
Cite as

Andrzej P. Wierzbicki. Reference Point Approaches and Objective Ranking. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 6501, pp. 1-20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{wierzbicki:DagSemProc.06501.2,
  author =	{Wierzbicki, Andrzej P.},
  title =	{{Reference Point Approaches and Objective Ranking}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--20},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{6501},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman Slowinski},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.06501.2},
  URN =		{urn:nbn:de:0030-drops-11210},
  doi =		{10.4230/DagSemProc.06501.2},
  annote =	{Keywords: Multiple criteria optimization and decisions; reference point approaches; objectivity and subjectivity in decision support}
}
Document
DOI: 10.4230/DagSemProc.04461.1
04461 Abstracts Collection – Practical Approaches to Multi-Objective Optimization

Authors: Jürgen Branke, Deb Kalyanmoy, Kaisa Miettinen, and Ralph E. Steuer

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
From 07.11.04 to 12.11.04, the Dagstuhl Seminar 04461 ``Practical Approaches to Multi-Objective Optimization'' 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.
Cite as

Jürgen Branke, Deb Kalyanmoy, Kaisa Miettinen, and Ralph E. Steuer. 04461 Abstracts Collection – Practical Approaches to Multi-Objective Optimization. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{branke_et_al:DagSemProc.04461.1,
  author =	{Branke, J\"{u}rgen and Kalyanmoy, Deb and Miettinen, Kaisa and Steuer, Ralph E.},
  title =	{{04461 Abstracts Collection – Practical Approaches to Multi-Objective Optimization}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--17},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.1},
  URN =		{urn:nbn:de:0030-drops-2551},
  doi =		{10.4230/DagSemProc.04461.1},
  annote =	{Keywords: Multi-objective optimization, evolutionary algorithms, decision support system}
}
Document
DOI: 10.4230/DagSemProc.04461.16
On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems

Authors: Oliver Schütze, Alessandro Dell'Aere, and Michael Dellnitz

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
In this report we describe how continuation methods can be used for the numerical treatment of multi-objective optimization problems (MOPs): starting with a given Karush-Kuhn-Tucker point (KKT-point) x of an MOP, these techniques can be applied to detect further KKT-points in the neighborhood of x. In the next step, again further points are computed starting with these new-found KKT-points, and so on. In order to maintain a good spread of these solutions we use boxes for the representation of the computed parts of the solution set. Based on this background, we propose a new predictor-corrector variant, and show some numerical results indicating the strength of the method, in particular in higher dimensions. Further, the data structure allows for an efficient computation of MOPs with more than two objectives, which has not been considered so far in most existing continuation methods.
Cite as

Oliver Schütze, Alessandro Dell'Aere, and Michael Dellnitz. On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{schutze_et_al:DagSemProc.04461.16,
  author =	{Sch\"{u}tze, Oliver and Dell'Aere, Alessandro and Dellnitz, Michael},
  title =	{{On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.16},
  URN =		{urn:nbn:de:0030-drops-3497},
  doi =		{10.4230/DagSemProc.04461.16},
  annote =	{Keywords: multi-objective optimization, continuation, k-manifolds}
}
Document
DOI: 10.4230/DagSemProc.04461.7
Application Issues for Multiobjective Evolutionary Algorithms

Authors: Thomas Hanne

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
In the talk, various issues of the design and application of multiobjective evolutionary algorithms for real-life optimization problems are discussed. In particular, questions on problem-specific data structures and evolutionary operators and the determination of method parameters are treated. Three application examples in the areas of constrained global optimization (electronic circuit design), semi-infinite programming (design centering problems), and discrete optimization (project scheduling) are discussed.
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Thomas Hanne. Application Issues for Multiobjective Evolutionary Algorithms. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{hanne:DagSemProc.04461.7,
  author =	{Hanne, Thomas},
  title =	{{Application Issues for Multiobjective Evolutionary Algorithms}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.7},
  URN =		{urn:nbn:de:0030-drops-3443},
  doi =		{10.4230/DagSemProc.04461.7},
  annote =	{Keywords: Multiobjective optimization, Pareto set, evolutionary algorithm, discrete optimization, continuous optimization, electronic circuit design, semi-infin}
}
Document
DOI: 10.4230/DagSemProc.04461.15
NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations

Authors: Enrico Rigoni and Silvia Poles

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
The NBI-NLPQLP optimization method is tested on several multi-objective optimization problems. Its performance is compared to that of MOGA-II: since NBI-NLPQLP is based on the classical gradient-based NLPQLP, it is fast and accurate, but not as robust, in comparison with the genetic algorithm. Furthermore a discontinuous Pareto frontier can give rise to problems in the NBI´s convergence. In order to overcome this problem, a hybridization technique coupled with a partitioning method is proposed.
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Enrico Rigoni and Silvia Poles. NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{rigoni_et_al:DagSemProc.04461.15,
  author =	{Rigoni, Enrico and Poles, Silvia},
  title =	{{NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--22},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.15},
  URN =		{urn:nbn:de:0030-drops-2728},
  doi =		{10.4230/DagSemProc.04461.15},
  annote =	{Keywords: Genetic Algorithms, Normal-Boundary Intersection, Designs optimizations}
}
Document
DOI: 10.4230/DagSemProc.04461.19
Runtime Analysis of a Simple Multi-Objective Evolutionary Algorithm

Authors: Oliver Giel

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
Practical knowledge on the design and application of multi-objective evolutionary algorithms (MOEAs) is available but well-founded theoretical analyses of the runtime are rare. Laumanns, Thiele, Zitzler, Welzel and Deb (2002) have started such an analysis for two simple mutation-based algorithms including SEMO. These algorithms search locally in the neighborhood of their current population by selecting an individual and flipping one randomly chosen bit. Due to its local search operator, SEMO cannot escape from local optima, and, therefore, has no finite expected runtime in general. In this talk, we investigate the runtime of a variant of SEMO whose mutation operator flips each bit independently. It is proven that its expected runtime is O(n^n) for all objective functions f: {0,1}^n -> R^m, and that there are bicriteria problems among the hardest problem for this algorithm. Moreover, for each d between 2 and n, a bicriteria problem with expected runtime Theta(n^d) is presented. This shows that bicriteria problems cover the full range of potential runtimes of this variant of SEMO. For the problem LOTZ (Leading-Ones-Trailing Zeroes), the runtime does not increase substantially if we use the global search operator. Finally, we consider the problem MOCO (Multi-Objective-Counting-Ones). We show that the conjectured bound O((n^2)log n) on the expected runtime is wrong for both variants of SEMO. In fact, MOCO is almost a worst case example for SEMO if we consider the expected runtime; however, the runtime is O((n^2)log n) with high probability. Some ideas from the proof will be presented.
Cite as

Oliver Giel. Runtime Analysis of a Simple Multi-Objective Evolutionary Algorithm. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{giel:DagSemProc.04461.19,
  author =	{Giel, Oliver},
  title =	{{Runtime Analysis of a Simple Multi-Objective Evolutionary Algorithm}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.19},
  URN =		{urn:nbn:de:0030-drops-2711},
  doi =		{10.4230/DagSemProc.04461.19},
  annote =	{Keywords: Runtime analysis, multi-objecive evolutionary algorithms}
}
Document
DOI: 10.4230/DagSemProc.04461.2
04461 Summary – Practical Approaches to Multi-Criterion Optimization

Authors: Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Ralph E. Steuer

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
Summary of the Dagstuhl Seminar 04461. Motivation, proceedings, achievements and feedback, future seminars
Cite as

Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Ralph E. Steuer. 04461 Summary – Practical Approaches to Multi-Criterion Optimization. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{branke_et_al:DagSemProc.04461.2,
  author =	{Branke, J\"{u}rgen and Deb, Kalyanmoy and Miettinen, Kaisa and Steuer, Ralph E.},
  title =	{{04461 Summary – Practical Approaches to Multi-Criterion Optimization}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.2},
  URN =		{urn:nbn:de:0030-drops-2430},
  doi =		{10.4230/DagSemProc.04461.2},
  annote =	{Keywords: Multi-criterion Optimization, Classical and Evolutionary Approaches}
}
Document
DOI: 10.4230/DagSemProc.04461.3
A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization

Authors: Jörg Fliege, Christoph Heermann, and Bernd Weyers

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
We present a new adaptive algorithm for convex quadratic multicriteria optimization. The algorithm is able to adaptively refine the approximation to the set of efficient points by way of a warm-start interior-point scalarization approach. Numerical results show that this technique is an order of magnitude faster than a standard method used for this problem.
Cite as

Jörg Fliege, Christoph Heermann, and Bernd Weyers. A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-39, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{fliege_et_al:DagSemProc.04461.3,
  author =	{Fliege, J\"{o}rg and Heermann, Christoph and Weyers, Bernd},
  title =	{{A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--39},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.3},
  URN =		{urn:nbn:de:0030-drops-2386},
  doi =		{10.4230/DagSemProc.04461.3},
  annote =	{Keywords: Multicriteria optimization, warm-start methods, interior-point methods, primal-dual algorithms}
}
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