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Documents authored by Kalyanmoy, Deb

Found 2 Possible Name Variants:

Kalyanmoy, Deb

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.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}
}

Deb, Kalyanmoy

Document
DOI: 10.4230/DagSemProc.09041.1
09041 Abstracts Collection – Hybrid and Robust Approaches to Multiobjective Optimization

Authors: Salvatore Greco, Kalyanmoy Deb, Kaisa Miettinen, and Eckart Zitzler

Published in: Dagstuhl Seminar Proceedings, Volume 9041, Hybrid and Robust Approaches to Multiobjective Optimization (2009)

Abstract
The seminar "Hybrid and Robust Approaches to Multiobjective Optimization" was a sequel to two previous Dagstuhl seminars (04461 in 2004 and 06501 in 2006). The main idea of this seminar series has been to bring together two contemporary fields related to multiobjective optimization – Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) -- to discuss critical research and application issues for bringing the entire field further and for fostering future collaboration. This particular seminar was participated by 53 researchers actively working in multiobjective optimization. The purpose of the seminar was to discuss two fundamental research topics related to multiobjective optimization: interactive methods requiring optimization and decision making aspects to be integrated for a practical implementation and robust multiobjective methodologies dealing with uncertainties in problem parameters, objectives, constraints and algorithms. The seminar was structured to have more emphasis on working group discussions, rather than individual presentations, so that the open and free environment and facilities of Schloss Dagstuhl could be fully utilized.
Cite as

Salvatore Greco, Kalyanmoy Deb, Kaisa Miettinen, and Eckart Zitzler. 09041 Abstracts Collection – Hybrid and Robust Approaches to Multiobjective Optimization. In Hybrid and Robust Approaches to Multiobjective Optimization. Dagstuhl Seminar Proceedings, Volume 9041, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{greco_et_al:DagSemProc.09041.1,
  author =	{Greco, Salvatore and Deb, Kalyanmoy and Miettinen, Kaisa and Zitzler, Eckart},
  title =	{{09041 Abstracts Collection – Hybrid and Robust Approaches to Multiobjective Optimization}},
  booktitle =	{Hybrid and Robust Approaches to Multiobjective Optimization},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9041},
  editor =	{Kalyanmoy Deb and Salvatore Greco and Kaisa Miettinen and Eckart Zitzler},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09041.1},
  URN =		{urn:nbn:de:0030-drops-20069},
  doi =		{10.4230/DagSemProc.09041.1},
  annote =	{Keywords: Multi-objective optimization, multiple criteria decision making, evolutionary multi-objective optimization, robust optimization, interactive optimization}
}
Document
DOI: 10.4230/DagSemProc.09041.2
09041 Summary – Hybrid and Robust Approaches to Multiobjective Optimization

Authors: Kalyanmoy Deb, Salvatore Greco, Kaisa Miettinen, and Eckart Zitzler

Published in: Dagstuhl Seminar Proceedings, Volume 9041, Hybrid and Robust Approaches to Multiobjective Optimization (2009)

Abstract
The seminar “Hybrid and Robust Approaches to Multiobjective Optimization” was a sequel to two previous Dagstuhl seminars (04461 in 2004 and 06501 in 2006). The main idea of this seminar series has been to bring together two contemporary fields related to multiobjective optimization – Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) – to discuss critical research and application issues for bringing the entire field further and for fostering future collaboration. This particular seminar was participated by 53 researchers actively working in multiobjective optimization. The purpose of the seminar was to discuss two fundamental research topics related to multiobjective optimization: interactive methods requiring optimization and decision making aspects to be integrated for a practical implementation and robust multiobjective methodologies dealing with uncertainties in problem parameters, objectives, constraints and algorithms. The seminar was structured to have more emphasis on working group discussions, rather than individual presentations, so that the open and free environment and facilities of Schloss Dagstuhl could be fully utilized.
Cite as

Kalyanmoy Deb, Salvatore Greco, Kaisa Miettinen, and Eckart Zitzler. 09041 Summary – Hybrid and Robust Approaches to Multiobjective Optimization. In Hybrid and Robust Approaches to Multiobjective Optimization. Dagstuhl Seminar Proceedings, Volume 9041, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{deb_et_al:DagSemProc.09041.2,
  author =	{Deb, Kalyanmoy and Greco, Salvatore and Miettinen, Kaisa and Zitzler, Eckart},
  title =	{{09041 Summary – Hybrid and Robust Approaches to Multiobjective Optimization}},
  booktitle =	{Hybrid and Robust Approaches to Multiobjective Optimization},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9041},
  editor =	{Kalyanmoy Deb and Salvatore Greco and Kaisa Miettinen and Eckart Zitzler},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09041.2},
  URN =		{urn:nbn:de:0030-drops-20054},
  doi =		{10.4230/DagSemProc.09041.2},
  annote =	{Keywords: Multi-objective optimization, multiple criteria decision making, evolutionary multi-objective optimization, robust optimization, interactive optimization}
}
Document
DOI: 10.4230/DagSemProc.09041.5
09041 Working Group on EMO for Robust Multiobjective Optimization (1st Round)

Authors: Joerg Fliege, Nicola Beume, Juergen Branke, Heinrich Braun, Nirupam Chakraborti, Kalyanmoy Deb, Sabine Helwig, Joshua Knowles, Martin Middendorf, Sanaz Mostaghim, Silvia Poles, Salazar Daniel, Shukla Pradymn, and El-Ghazli Talbi

Published in: Dagstuhl Seminar Proceedings, Volume 9041, Hybrid and Robust Approaches to Multiobjective Optimization (2009)

Abstract
This group explored various robust methodologies for multiobjective optimization.
Cite as

Joerg Fliege, Nicola Beume, Juergen Branke, Heinrich Braun, Nirupam Chakraborti, Kalyanmoy Deb, Sabine Helwig, Joshua Knowles, Martin Middendorf, Sanaz Mostaghim, Silvia Poles, Salazar Daniel, Shukla Pradymn, and El-Ghazli Talbi. 09041 Working Group on EMO for Robust Multiobjective Optimization (1st Round). In Hybrid and Robust Approaches to Multiobjective Optimization. Dagstuhl Seminar Proceedings, Volume 9041, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{fliege_et_al:DagSemProc.09041.5,
  author =	{Fliege, Joerg and Beume, Nicola and Branke, Juergen and Braun, Heinrich and Chakraborti, Nirupam and Deb, Kalyanmoy and Helwig, Sabine and Knowles, Joshua and Middendorf, Martin and Mostaghim, Sanaz and Poles, Silvia and Salazar Daniel and Shukla Pradymn and Talbi, El-Ghazli},
  title =	{{09041 Working Group on EMO for Robust Multiobjective Optimization (1st Round)}},
  booktitle =	{Hybrid and Robust Approaches to Multiobjective Optimization},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9041},
  editor =	{Kalyanmoy Deb and Salvatore Greco and Kaisa Miettinen and Eckart Zitzler},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09041.5},
  URN =		{urn:nbn:de:0030-drops-20030},
  doi =		{10.4230/DagSemProc.09041.5},
  annote =	{Keywords: Robust multiobjective optimization}
}
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.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.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.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.04461.5
A Tutorial on Evolutionary Multi-Objective Optimization (EMO)

Authors: Kalyanmoy Deb

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

Abstract
Many real-world search and optimization problems are naturally posed as non-linear programming problems having multiple objectives. Due to lack of suitable solution techniques, such problems are artificially converted into a single-objective problem and solved. The difficulty arises because such problems give rise to a set of Pareto-optimal solutions, instead of a single optimum solution. It then becomes important to find not just one Pareto-optimal solution but as many of them as possible. Classical methods are not quite efficient in solving these problems because they require repetitive applications to find multiple Pareto-optimal solutions and in some occasions repetitive applications do not guarantee finding distinct Pareto-optimal solutions. The population approach of evolutionary algorithms (EAs) allows an efficient way to find multiple Pareto-optimal solutions simultaneously in a single simulation run. In this tutorial, we discussed the following aspects related to EMO: 1. The basic differences in principle of EMO with classical methods. 2. A gentle introduction to evolutionary algorithms with simple examples. A simple method of handling constraints was also discussed. 3. The concept of domination and methods of finding non-dominated solutions in a population of solutions were discussed. 4. A brief history of the development of EMO is highlighted. 5. A number of main EMO methods (NSGA-II, SPEA and PAES) were discussed. 6. The advantage of EMO methodologies was discussed by presenting a number of case studies. They clearly showed the advantage of finding a number of Pareto-optimal solutions simultaneously. 7. Three advantages of using an EMO methodology were stressed: (i) For a better decision making (in terms of choosing a compromised solution) in the presence of multiple solutions (ii) For finding important relationships among decision variables (useful in design optimization). Some case studies from engineering demonstrated the importance of such studies. (iii) For solving other optimization problems efficiently. For example, in solving genetic programming problems, the so-called `bloating problem of increased program size can be solved by using a second objective of minimizing the size of the programs. 8. A number of salient research topics were highlighted. Some of them are as follows: (i) Development of scalable test problems (ii) Development of computationally fast EMO methods (iii) Performance metrics for evaluating EMO methods (iv) Interactive EMO methodologies (v) Robust multi-objective optimization procedures (vi) Finding knee or other important solutions including partial Pareto-optimal set (vii) Multi-objective scheduling and other optimization problems. It was clear from the discussions that evolutionary search methods offers an alternate means of solving multi-objective optimization problems compared to classical approaches. This is why multi-objective optimization using EAs is getting a growing attention in the recent years. The motivated readers may explore current research issues and other important studies from various texts (Coello et al, 2003; Deb, 2001), conference proceedings (EMO-01 and EMO-03 Proceedings) and numerous research papers (http://www.lania.mx/~ccoello/EMOO/). References: ---------- C. A. C. Coello, D. A. VanVeldhuizen, and G. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Boston, MA: Kluwer Academic Publishers, 2002. K.Deb. Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley, 2001. C. Fonseca, P. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors. Proceedings of the Second Evolutionary Multi-Criterion Optimization (EMO-03) Conference (Lecture Notes in Computer Science (LNCS) 2632). Heidelberg: Springer, 2003. E. Zitzler, K. Deb, L. Thiele, C. A. C. Coello, and D. Corne, editors. Proceedings of the First Evolutionary Multi-Criterion Optimization (EMO-01) Conference (Lecture Notes in Computer Science (LNCS) 1993). Heidelberg: Springer, 2001.
Cite as

Kalyanmoy Deb. A Tutorial on Evolutionary Multi-Objective Optimization (EMO). In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{deb:DagSemProc.04461.5,
  author =	{Deb, Kalyanmoy},
  title =	{{A Tutorial on Evolutionary Multi-Objective Optimization (EMO)}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--2},
  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.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.5},
  URN =		{urn:nbn:de:0030-drops-2520},
  doi =		{10.4230/DagSemProc.04461.5},
  annote =	{Keywords: Multi-objective optimization, multi-criterion optimization, Pareto-optimal solutions, Evolutionary methods, EMO}
}
Document
DOI: 10.4230/DagSemProc.04461.17
On Properly Pareto Optimal Solutions

Authors: Pradyumn Kumar Shukla, Joydeep Dutta, and Kalyanmoy Deb

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

Abstract
In this paper we study epsilon-proper efficiency in multiobjective optimization. We introduce various new definitions of epsilon-proper efficiency, relate them with existing ones, study various concepts and develop very general necessary optimality conditions for a few of them.
Cite as

Pradyumn Kumar Shukla, Joydeep Dutta, and Kalyanmoy Deb. On Properly Pareto Optimal Solutions. 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{shukla_et_al:DagSemProc.04461.17,
  author =	{Shukla, Pradyumn Kumar and Dutta, Joydeep and Deb, Kalyanmoy},
  title =	{{On Properly Pareto Optimal Solutions}},
  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.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.17},
  URN =		{urn:nbn:de:0030-drops-2403},
  doi =		{10.4230/DagSemProc.04461.17},
  annote =	{Keywords: Proper efficiency, epsilon solutions}
}
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