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Documents authored by Fonseca Carlos

Found 2 Possible Name Variants:

Fonseca, Carlos M.

Document
DOI: 10.4230/DagRep.13.9.1
Multiobjective Optimization on a Budget (Dagstuhl Seminar 23361)

Authors: Richard Allmendinger, Carlos M. Fonseca, Serpil Sayin, Margaret M. Wiecek, and Michael Stiglmayr

Published in: Dagstuhl Reports, Volume 13, Issue 9 (2024)

Abstract
The Dagstuhl Seminar 23361 Multiobjective Optimization on a Budget carried on a series of seven previous Dagstuhl Seminars (04461, 06501, 09041, 12041, 15031, 18031, 20031) focused on Multiobjective Optimization. The original goal of this series has been 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 seminar particularly focused on the case where the approaches from both communities may be challenged by limited resources. This report documents the program and the outcomes of Dagstuhl Seminar 23361 "Multiobjective Optimization on a Budget". Three major types of resource limitations were highlighted during the seminar: methodological, technical and human related. The effect of these limitations on optimization and decision-making quality, as well as methods to quantify and mitigate this influence, were considered in different working groups.
Cite as

Richard Allmendinger, Carlos M. Fonseca, Serpil Sayin, Margaret M. Wiecek, and Michael Stiglmayr. Multiobjective Optimization on a Budget (Dagstuhl Seminar 23361). In Dagstuhl Reports, Volume 13, Issue 9, pp. 1-68, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{allmendinger_et_al:DagRep.13.9.1,
  author =	{Allmendinger, Richard and Fonseca, Carlos M. and Sayin, Serpil and Wiecek, Margaret M. and Stiglmayr, Michael},
  title =	{{Multiobjective Optimization on a Budget (Dagstuhl Seminar 23361)}},
  pages =	{1--68},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{9},
  editor =	{Allmendinger, Richard and Fonseca, Carlos M. and Sayin, Serpil and Wiecek, Margaret M. and Stiglmayr, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.9.1},
  URN =		{urn:nbn:de:0030-drops-198207},
  doi =		{10.4230/DagRep.13.9.1},
  annote =	{Keywords: evolutionary algorithms, expensive optimization, few-shot learning, machine learning, optimization, simulation}
}
Document
DOI: 10.4230/DagRep.12.2.87
Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081)

Authors: Anne Auger, Carlos M. Fonseca, Tobias Friedrich, Johannes Lengler, and Armand Gissler

Published in: Dagstuhl Reports, Volume 12, Issue 2 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22081 "Theory of Randomized Optimization Heuristics". This seminar is part of a biennial seminar series. This year, we focused on connections between classical topics of the community, such as Evolutionary Algorithms and Strategies (EA, ES), Estimation-of-Distribution Algorithms (EDA) and Evolutionary Multi-Objective Optimization (EMO), and related fields like Stochastic Gradient Descent (SGD) and Bayesian Optimization (BO). The mixture proved to be extremely successful. Already the first talk turned into a two hour long, vivid and productive plenary discussion. The seminar was smaller than previous versions (due to corona regulations), but its intensity more than made up for the smaller size.
Cite as

Anne Auger, Carlos M. Fonseca, Tobias Friedrich, Johannes Lengler, and Armand Gissler. Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081). In Dagstuhl Reports, Volume 12, Issue 2, pp. 87-102, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{auger_et_al:DagRep.12.2.87,
  author =	{Auger, Anne and Fonseca, Carlos M. and Friedrich, Tobias and Lengler, Johannes and Gissler, Armand},
  title =	{{Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081)}},
  pages =	{87--102},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{2},
  editor =	{Auger, Anne and Fonseca, Carlos M. and Friedrich, Tobias and Lengler, Johannes and Gissler, Armand},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.87},
  URN =		{urn:nbn:de:0030-drops-169325},
  doi =		{10.4230/DagRep.12.2.87},
  annote =	{Keywords: black-box optimization, derivative-free optimization, evolutionary and genetic algorithms, randomized search algorithms, stochastic gradient descent, theoretical computer science}
}
Document
DOI: 10.4230/DagRep.10.1.52
Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031)

Authors: Carlos M. Fonseca, Kathrin Klamroth, Günter Rudolph, and Margaret M. Wiecek

Published in: Dagstuhl Reports, Volume 10, Issue 1 (2020)

Abstract
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.
Cite as

Carlos M. Fonseca, Kathrin Klamroth, Günter Rudolph, and Margaret M. Wiecek. Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031). In Dagstuhl Reports, Volume 10, Issue 1, pp. 52-129, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{fonseca_et_al:DagRep.10.1.52,
  author =	{Fonseca, Carlos M. and Klamroth, Kathrin and Rudolph, G\"{u}nter and Wiecek, Margaret M.},
  title =	{{Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031)}},
  pages =	{52--129},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{10},
  number =	{1},
  editor =	{Fonseca, Carlos M. and Klamroth, Kathrin and Rudolph, G\"{u}nter and Wiecek, Margaret M.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.10.1.52},
  URN =		{urn:nbn:de:0030-drops-124017},
  doi =		{10.4230/DagRep.10.1.52},
  annote =	{Keywords: multiple criteria decision making, evolutionary multiobjective optimization, scalability}
}
Document
DOI: 10.4230/DagRep.9.10.61
Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431)

Authors: Carola Doerr, Carlos M. Fonseca, Tobias Friedrich, and Xin Yao

Published in: Dagstuhl Reports, Volume 9, Issue 10 (2020)

Abstract
This report documents the activities of Dagstuhl Seminar 19431 on Theory of Randomized Optimization Heuristics. 46 researchers from Europe, Australia, Asia, and North America have come together to discuss ongoing research. This tenth edition of the seminar series had three focus topics: (1) relation between optimal control and heuristic optimization, (2) benchmarking optimization heuristics, and (3) the interfaces between continuous and discrete optimization. Several breakout sessions have provided ample opportunity to brainstorm on recent developments in the research landscape, to discuss and solve open problems, and to kick-start new research initiatives.
Cite as

Carola Doerr, Carlos M. Fonseca, Tobias Friedrich, and Xin Yao. Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431). In Dagstuhl Reports, Volume 9, Issue 10, pp. 61-94, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{doerr_et_al:DagRep.9.10.61,
  author =	{Doerr, Carola and Fonseca, Carlos M. and Friedrich, Tobias and Yao, Xin},
  title =	{{Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431)}},
  pages =	{61--94},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{10},
  editor =	{Doerr, Carola and Fonseca, Carlos M. and Friedrich, Tobias and Yao, Xin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.10.61},
  URN =		{urn:nbn:de:0030-drops-118567},
  doi =		{10.4230/DagRep.9.10.61},
  annote =	{Keywords: algorithms and complexity, evolutionary algorithms, machine learning, optimization, soft computing}
}
Document
DOI: 10.4230/DagSemProc.04461.14
Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms

Authors: Carlos M. Fonseca and Peter J. Fleming

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

Abstract
In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including constraint satisfaction, lexicographic optimization, and a form of goal programming. Then, the ranking of an arbitrary number of candidates is considered, and the effect of preference changes on the cost surface seen by an evolutionary algorithm is illustrated graphically for a simple problem. The formulation of a multiobjective genetic algorithm based on the proposed decision strategy is also discussed. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Finally, an application to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine is described, which illustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The two instances of the problem studied demonstrate the need for preference articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample effectively. It is shown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to supply preference information to the GA.
Cite as

Carlos M. Fonseca and Peter J. Fleming. Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms. 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{fonseca_et_al:DagSemProc.04461.14,
  author =	{Fonseca, Carlos M. and Fleming, Peter J.},
  title =	{{Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms}},
  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.04461.14},
  URN =		{urn:nbn:de:0030-drops-2371},
  doi =		{10.4230/DagSemProc.04461.14},
  annote =	{Keywords: Evolutionary algorithms, multiobjective optimization, preference articulation, interactive optimization.}
}

Fonseca, José Carlos

Document
DOI: 10.4230/OASIcs.WCET.2014.63
The Challenge of Time-Predictability in Modern Many-Core Architectures

Authors: Vincent Nélis, Patrick Meumeu Yomsi, Luís Miguel Pinho, José Carlos Fonseca, Marko Bertogna, Eduardo Quiñones, Roberto Vargas, and Andrea Marongiu

Published in: OASIcs, Volume 39, 14th International Workshop on Worst-Case Execution Time Analysis (2014)

Abstract
The recent technological advancements and market trends are causing an interesting phenomenon towards the convergence of High-Performance Computing (HPC) and Embedded Computing (EC) domains. Many recent HPC applications require huge amounts of information to be processed within a bounded amount of time while EC systems are increasingly concerned with providing higher performance in real-time. The convergence of these two domains towards systems requiring both high performance and a predictable time-behavior challenges the capabilities of current hardware architectures. Fortunately, the advent of next-generation many-core embedded platforms has the chance of intercepting this converging need for predictability and high-performance, allowing HPC and EC applications to be executed on efficient and powerful heterogeneous architectures integrating general-purpose processors with many-core computing fabrics. However, addressing this mixed set of requirements is not without its own challenges and it is now of paramount importance to develop new techniques to exploit the massively parallel computation capabilities of many-core platforms in a predictable way.
Cite as

Vincent Nélis, Patrick Meumeu Yomsi, Luís Miguel Pinho, José Carlos Fonseca, Marko Bertogna, Eduardo Quiñones, Roberto Vargas, and Andrea Marongiu. The Challenge of Time-Predictability in Modern Many-Core Architectures. In 14th International Workshop on Worst-Case Execution Time Analysis. Open Access Series in Informatics (OASIcs), Volume 39, pp. 63-72, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{nelis_et_al:OASIcs.WCET.2014.63,
  author =	{N\'{e}lis, Vincent and Yomsi, Patrick Meumeu and Pinho, Lu{\'\i}s Miguel and Fonseca, Jos\'{e} Carlos and Bertogna, Marko and Qui\~{n}ones, Eduardo and Vargas, Roberto and Marongiu, Andrea},
  title =	{{The Challenge of Time-Predictability in Modern Many-Core Architectures}},
  booktitle =	{14th International Workshop on Worst-Case Execution Time Analysis},
  pages =	{63--72},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-69-9},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{39},
  editor =	{Falk, Heiko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.WCET.2014.63},
  URN =		{urn:nbn:de:0030-drops-46050},
  doi =		{10.4230/OASIcs.WCET.2014.63},
  annote =	{Keywords: Time-Predictability, Many-Cores, Multi-Cores, Timing Analysis}
}
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