The Experts below are selected from a list of 27378 Experts worldwide ranked by ideXlab platform
Kaisa Miettinen - One of the best experts on this subject based on the ideXlab platform.
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An Application of Multiobjective Optimization to Process Simulation
2012Co-Authors: Jussi Hakanen, Kaisa Miettinen, Marko M. MäkeläAbstract:We study Multiobjective Optimization problems arising from chemical process simulation. Interactive Multiobjective Optimization method NIMBUS, developed at the University of Jyvaskyla, is combined ...
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Learning in Multiobjective Optimization (Dagstuhl Seminar 12041)
2012Co-Authors: Salvatore Greco, Kaisa Miettinen, Joshua Knowles, Eckart ZitzlerAbstract:This report documents the programme and outcomes of the Dagstuhl Seminar 12041 "Learning in Multiobjective Optimization". The purpose of the seminar was to bring together researchers from the two main communities studying Multiobjective Optimization, Multiple Criteria Decision Making and Evolutionary Multiobjective Optimization, to take part in a wide-ranging discussion of what constitutes learning in Multiobjective Optimization, how it can be facilitated, and how it can be measured. The outcome was a deeper, more integrated understanding of the whole problem-solving process in Multiobjective Optimization from the viewpoint of learning, and several concrete research projects directly addressing different aspects of learning.
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Connections Between Single-Level and Bilevel Multiobjective Optimization
Journal of Optimization Theory and Applications, 2011Co-Authors: Sauli Ruuska, Kaisa Miettinen, Margaret M. WiecekAbstract:The relationship between bilevel Optimization and Multiobjective Optimization has been studied by several authors, and there have been repeated attempts to establish a link between the two. We unify the results from the literature and generalize them for bilevel Multiobjective Optimization. We formulate sufficient conditions for an arbitrary binary relation to guarantee equality between the efficient set produced by the relation and the set of optimal solutions to a bilevel problem. In addition, we present specially structured bilevel Multiobjective Optimization problems motivated by real-life applications and an accompanying binary relation permitting their reduction to single-level Multiobjective Optimization problems.
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Hybrid and Robust Approaches to Multiobjective Optimization - 09041 Summary - Hybrid and Robust Approaches to Multiobjective Optimization.
2009Co-Authors: Kalyanmoy Deb, Kaisa Miettinen, Salvatore Greco, Eckart ZitzlerAbstract: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.
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Hybrid and Robust Approaches to Multiobjective Optimization - 09041 Abstracts Collection -- Hybrid and Robust Approaches to Multiobjective Optimization
2009Co-Authors: Salvatore Greco, Kalyanmoy Deb, Kaisa Miettinen, Eckart ZitzlerAbstract: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.
Günter Rudolph - One of the best experts on this subject based on the ideXlab platform.
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Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031)
2020Co-Authors: Carlos M. Fonseca, Kathrin Klamroth, Günter Rudolph, Margaret M. WiecekAbstract:The Dagstuhl Seminar 20031 Scalability in Multiobjective Optimization carried on a series of six previous Dagstuhl Seminars (04461, 06501, 09041, 12041, 15031 and 18031) that were focused on Multiobjective Optimization. The continuing goal of this series is to strengthen the links between the Evolutionary Multiobjective Optimization (EMO) and the Multiple Criteria Decision Making (MCDM) communities, two of the largest communities concerned with Multiobjective Optimization today. This report documents the program and the outcomes of Dagstuhl Seminar 20031 "Scalability in Multiobjective Optimization". The seminar focused on three main aspects of scalability in Multiobjective Optimization (MO) and their interplay, namely (1) MO with many objective functions, (2) MO with many decision makers, and (3) MO with many variables and large amounts of data.
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Personalized Multiobjective Optimization: An Analytics Perspective (Dagstuhl Seminar 18031)
2018Co-Authors: Kathrin Klamroth, Günter Rudolph, Joshua Knowles, Margaret M. WiecekAbstract:The Dagstuhl Seminar 18031 Personalization in Multiobjective Optimization: An Analytics Perspective carried on a series of five previous Dagstuhl Seminars (04461, 06501, 09041, 12041 and 15031) that were focused on Multiobjective Optimization. The continuing goal of this series is to strengthen the links between the Evolutionary Multiobjective Optimization (EMO) and the Multiple Criteria Decision Making (MCDM) communities, two of the largest communities concerned with Multiobjective Optimization today. Personalization in Multiobjective Optimization, the topic of this seminar, was motivated by the scientific challenges generated by personalization, mass customization, and mass data, and thus crosslinks application challenges with research domains integrating all aspects of EMO and MCDM. The outcome of the seminar was a new perspective on the opportunities as well as the research requirements for Multiobjective Optimization in the thriving fields of data analytics and personalization. Several multi-disciplinary research projects and new collaborations were initiated during the seminar, further interlacing the two communities of EMO and MCDM.
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Editorial: Special Issue on Understanding Complexity in Multiobjective Optimization
Journal of Multi-Criteria Decision Analysis, 2017Co-Authors: Salvatore Greco, Kathrin Klamroth, Joshua Knowles, Günter RudolphAbstract:This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in Multiobjective Optimization. The outcome was a clarified viewpoint of complexity in the various facets of Multiobjective Optimization, leading to several research initiatives with innovative approaches for coping with complexity. Seminar January 11–16, 2015 – http://www.dagstuhl.de/15031 1998 ACM Subject Classification G.1.6 Optimization, H.4.2 Types of Systems, I.2.6 Learning, I.2.8 Problem Solving, Control Methods, and Search, I.5.1 Models
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Understanding Complexity in Multiobjective Optimization (Dagstuhl Seminar 15031)
2015Co-Authors: Salvatore Greco, Kathrin Klamroth, Joshua Knowles, Günter RudolphAbstract:This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in Multiobjective Optimization. The outcome was a clarified viewpoint of complexity in the various facets of Multiobjective Optimization, leading to several research initiatives with innovative approaches for coping with complexity.
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Multiobjective Optimization - Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization, 2008Co-Authors: El-ghazali Talbi, Günter Rudolph, Sanaz Mostaghim, Tatsuya Okabe, Hisao Ishibuchi, Carlos A. Coello CoelloAbstract:This chapter presents a general overview of parallel approaches for Multiobjective Optimization. For this purpose, we propose a taxonomy for parallel metaheuristics and exact methods. This chapter covers the design aspect of the algorithms as well as the implementation aspects on different parallel and distributed architectures.
Eckart Zitzler - One of the best experts on this subject based on the ideXlab platform.
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Evolutionary Multiobjective Optimization
Handbook of Natural Computing, 2012Co-Authors: Eckart ZitzlerAbstract:Very often real-world applications have several multiple conflicting\nobjectives. Recently there has been a growing interest in evolutionary\nMultiobjective Optimization algorithms that combine two major disciplines:\nevolutionary computation and the theoretical frameworks of multicriteria\ndecision making. In this introductory chapter, some fundamental concepts\nof Multiobjective Optimization are introduced, emphasizing the motivation\nand advantages of using evolutionary algorithms. We then lay out\nthe important contributions of the remaining chapters of this volume.
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Learning in Multiobjective Optimization (Dagstuhl Seminar 12041)
2012Co-Authors: Salvatore Greco, Kaisa Miettinen, Joshua Knowles, Eckart ZitzlerAbstract:This report documents the programme and outcomes of the Dagstuhl Seminar 12041 "Learning in Multiobjective Optimization". The purpose of the seminar was to bring together researchers from the two main communities studying Multiobjective Optimization, Multiple Criteria Decision Making and Evolutionary Multiobjective Optimization, to take part in a wide-ranging discussion of what constitutes learning in Multiobjective Optimization, how it can be facilitated, and how it can be measured. The outcome was a deeper, more integrated understanding of the whole problem-solving process in Multiobjective Optimization from the viewpoint of learning, and several concrete research projects directly addressing different aspects of learning.
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Hybrid and Robust Approaches to Multiobjective Optimization - 09041 Summary - Hybrid and Robust Approaches to Multiobjective Optimization.
2009Co-Authors: Kalyanmoy Deb, Kaisa Miettinen, Salvatore Greco, Eckart ZitzlerAbstract: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.
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Hybrid and Robust Approaches to Multiobjective Optimization - 09041 Abstracts Collection -- Hybrid and Robust Approaches to Multiobjective Optimization
2009Co-Authors: Salvatore Greco, Kalyanmoy Deb, Kaisa Miettinen, Eckart ZitzlerAbstract: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.
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Hybrid and Robust Approaches to Multiobjective Optimization - 09041 Working Group on EMO for Interactive Multiobjective Optimization (1st Round).
2009Co-Authors: Carlos M. Fonseca, Xavier Gandibleux, Pekka Korhonen, Luis Marti, Boris Naujoks, Lothar Thiele, Jyrki Wallenius, Eckart ZitzlerAbstract:This group explored the use of EMO in an interactive manner to solve Multiobjective Optimization problems.
Salvatore Greco - One of the best experts on this subject based on the ideXlab platform.
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Editorial: Special Issue on Understanding Complexity in Multiobjective Optimization
Journal of Multi-Criteria Decision Analysis, 2017Co-Authors: Salvatore Greco, Kathrin Klamroth, Joshua Knowles, Günter RudolphAbstract:This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in Multiobjective Optimization. The outcome was a clarified viewpoint of complexity in the various facets of Multiobjective Optimization, leading to several research initiatives with innovative approaches for coping with complexity. Seminar January 11–16, 2015 – http://www.dagstuhl.de/15031 1998 ACM Subject Classification G.1.6 Optimization, H.4.2 Types of Systems, I.2.6 Learning, I.2.8 Problem Solving, Control Methods, and Search, I.5.1 Models
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Understanding Complexity in Multiobjective Optimization (Dagstuhl Seminar 15031)
2015Co-Authors: Salvatore Greco, Kathrin Klamroth, Joshua Knowles, Günter RudolphAbstract:This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in Multiobjective Optimization. The outcome was a clarified viewpoint of complexity in the various facets of Multiobjective Optimization, leading to several research initiatives with innovative approaches for coping with complexity.
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Learning in Multiobjective Optimization (Dagstuhl Seminar 12041)
2012Co-Authors: Salvatore Greco, Kaisa Miettinen, Joshua Knowles, Eckart ZitzlerAbstract:This report documents the programme and outcomes of the Dagstuhl Seminar 12041 "Learning in Multiobjective Optimization". The purpose of the seminar was to bring together researchers from the two main communities studying Multiobjective Optimization, Multiple Criteria Decision Making and Evolutionary Multiobjective Optimization, to take part in a wide-ranging discussion of what constitutes learning in Multiobjective Optimization, how it can be facilitated, and how it can be measured. The outcome was a deeper, more integrated understanding of the whole problem-solving process in Multiobjective Optimization from the viewpoint of learning, and several concrete research projects directly addressing different aspects of learning.
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Interactive evolutionary Multiobjective Optimization driven by robust ordinal regression
Bulletin of the Polish Academy of Sciences: Technical Sciences, 2010Co-Authors: Jurgen Branke, Salvatore Greco, Roman Słowiński, Piotr ZielniewiczAbstract:Interactive evolutionary Multiobjective Optimization driven by robust ordinal regressionThis paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary Multiobjective Optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive value functions compatible with this preference information is used within a properly modified version of the evolutionary Multiobjective Optimization technique NSGA-II in order to focus the search towards solutions satisfying the preferences of the decision maker. This allows to speed up convergence to the most preferred region of the Pareto-front.
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Hybrid and Robust Approaches to Multiobjective Optimization - 09041 Summary - Hybrid and Robust Approaches to Multiobjective Optimization.
2009Co-Authors: Kalyanmoy Deb, Kaisa Miettinen, Salvatore Greco, Eckart ZitzlerAbstract: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.
Carlos A. Coello Coello - One of the best experts on this subject based on the ideXlab platform.
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Multiobjective Optimization and Artificial Immune Systems: A Review
Handbook of Research on Artificial Immune Systems and Natural Computing, 2009Co-Authors: Fabio Freschi, Carlos A. Coello Coello, M. RepettoAbstract:This chapter aims to review the state of the art in algorithms of Multiobjective Optimization with artificial immune systems (MOAIS). As it will be focused in the chapter, Artificial Immune Systems (AIS) have some intrinsic characteristics which make them well suited as Multiobjective Optimization algorithms. Following this basic idea, different implementations have been proposed in the literature. This chapter aims to provide a thorough review of the literature on Multiobjective Optimization algorithms based on the emulation of the immune system.
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Multiobjective Optimization - Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization, 2008Co-Authors: El-ghazali Talbi, Günter Rudolph, Sanaz Mostaghim, Tatsuya Okabe, Hisao Ishibuchi, Carlos A. Coello CoelloAbstract:This chapter presents a general overview of parallel approaches for Multiobjective Optimization. For this purpose, we propose a taxonomy for parallel metaheuristics and exact methods. This chapter covers the design aspect of the algorithms as well as the implementation aspects on different parallel and distributed architectures.
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GECCO - Multiobjective Optimization using ideas from the clonal selection principle
Genetic and Evolutionary Computation — GECCO 2003, 2003Co-Authors: Nareli Cruz Cortés, Carlos A. Coello CoelloAbstract:In this paper, we propose a new Multiobjective Optimization approach based on the clonal selection principle. Our approach is compared with respect to other evolutionary Multiobjective Optimization techniques that are representative of the state-of-the-art in the area. In our study, several test functions and metrics commonly adopted in evolutionary Multiobjective Optimization are used. Our results indicate that the use of an artificial immune system for Multiobjective Optimization is a viable alternative.
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Evolutionary Multiobjective Optimization: Current and Future Challenges
Advances in Soft Computing, 2003Co-Authors: Carlos A. Coello CoelloAbstract:In this paper, we will briefly discuss the current state of the research on evolutionary Multiobjective Optimization, emphasizing the main achievements obtained to date. Achievements in algorithmic design are discussed from its early origins until the current approaches which are considered as the “second generation” in evolutionary Multiobjective Optimization. Some relevant applications are discussed as well, and we conclude with a list of future challenges for researchers working (or planning to work) in this area in the next few years.
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EMO - A Short Tutorial on Evolutionary Multiobjective Optimization
Lecture Notes in Computer Science, 2001Co-Authors: Carlos A. Coello CoelloAbstract:This tutorial will review some of the basic concepts related to evolutionary Multiobjective Optimization (i.e., the use of evolutionary algorithms to handle more than one objective function at a time). The most commonly used evolutionary Multiobjective Optimization techniques will be described and criticized, including some of their applications. Theory, test functions and metrics will be also discussed. Finally, we will provide some possible paths of future research in this area.
