Search

Your search keyword '"Tobias Wagner"' showing total 11 results
Start Over Author "Tobias Wagner" Publisher hal ccsd
11 results on '"Tobias Wagner"'

1. Mix&Match: Towards Omitting Modelling Through In-situ Remixing of Model Repository Artifacts in Mixed Reality

Author

Jan Gugenheimer, Evgeny Stemasov, Tobias Wagner, Enrico Rukzio, Universität Ulm - Ulm University [Ulm, Allemagne], Télécom ParisTech, Institut Polytechnique de Paris (IP Paris), Design, Interaction, Visualization & Applications (DIVA), Laboratoire Traitement et Communication de l'Information (LTCI), and Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris

Subjects
Core (game theory), Human–computer interaction, Computer science, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 020207 software engineering, 0501 psychology and cognitive sciences, 02 engineering and technology, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Engineering design process, 050107 human factors, Mixed reality, ComputingMilieux_MISCELLANEOUS
Abstract

The accessibility of tools to model artifacts is one of the core driving factors for the adoption of Personal Fabrication. Subsequently, model repositories like Thingiverse became important tools in (novice) makers' processes. They allow them to shorten or even omit the design process, offloading a majority of the effort to other parties. However, steps like measurement of surrounding constraints (e.g., clearance) which exist only inside the users' environment, can not be similarly outsourced. We propose Mix&Match a mixed-reality-based system which allows users to browse model repositories, preview the models in-situ, and adapt them to their environment in a simple and immediate fashion. Mix&Match aims to provide users with CSG operations which can be based on both virtual and real geometry. We present interaction patterns and scenarios for Mix&Match, arguing for the combination of mixed reality and model repositories. This enables almost modelling-free personal fabrication for both novices and expert makers.

Published
2020

2. Benchmarking MATLAB's gamultiobj (NSGA-II) on the Bi-objective BBOB-2016 Test Suite

Author

Tea Tušar, Tobias Wagner, Dejan Tušar, Anne Auger, Nikolaus Hansen, Dimo Brockhoff, Machine Learning and Optimisation (TAO), Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dortmund [Dortmund] (TU), ANR-12-MONU-0009,NumBBO,Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire(2012), Machine Learning and Optimisation ( TAO ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Parallel Cooperative Multi-criteria Optimization ( DOLPHIN ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Technische Universität Dortmund [Dortmund] ( TU ), ANR : ANR-2012-MONU-0009,NumBBO, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC], Computer science, 0102 computer and information sciences, 02 engineering and technology, Benchmarking, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], computer.software_genre, 01 natural sciences, Toolbox, 010201 computation theory & mathematics, Black-box optimization, 0202 electrical engineering, electronic engineering, information engineering, Test suite, Bi objective, Benchmark (computing), 020201 artificial intelligence & image processing, Data mining, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Bi-objective optimization, MATLAB, computer, computer.programming_language
Abstract

International audience; In this paper, we benchmark a variant of the well-known NSGA-II algorithm of Deb et al. on the biobjective bbob-biobj test suite of the Comparing Continuous Optimizers platform COCO. To this end, we employ the implementation of MATLAB's gamultiobj toolbox with its default settings and a population size of 100.

Published
2016

3. The Impact of Search Volume on the Performance of RANDOMSEARCH on the Bi-objective BBOB-2016 Test Suite

Author

Dejan Tušar, Nikolaus Hansen, Dimo Brockhoff, Tobias Wagner, Anne Auger, Tea Tušar, Machine Learning and Optimisation (TAO), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dortmund [Dortmund] (TU), ANR-12-MONU-0009,NumBBO,Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire(2012), Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Machine Learning and Optimisation ( TAO ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Parallel Cooperative Multi-criteria Optimization ( DOLPHIN ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Technische Universität Dortmund [Dortmund] ( TU ), and ANR : ANR-2012-MONU-0009,NumBBO

Subjects
[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC], 021103 operations research, 0211 other engineering and technologies, Pareto principle, Sampling (statistics), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Set (abstract data type), Random search, Benchmarking, Dimension (vector space), Search algorithm, Black-box optimization, 0202 electrical engineering, electronic engineering, information engineering, Test suite, 020201 artificial intelligence & image processing, Hypercube, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Bi-objective optimization, Algorithm, Mathematics
Abstract

International audience; Pure random search is undeniably the simplest stochastic search algorithm for numerical optimization. Essentially the only thing to be determined to implement the algorithm is its sampling space, the influence of which on the performance on the bi-objective bbob-biobj test suite of the COCO platform is investigated here. It turns out that the suggested region of interest of [−100, 100] n (with n being the problem dimension) has a too vast volume for the algorithm to approximate the Pareto set effectively. Better performance can be achieved if solutions are sampled uniformly within [−5, 5] n or [−4, 4] n. The latter sampling box corresponds to the smallest hypercube encapsulating all single-objective optima of the 55 constructed bi-objective problems of the bbob-biobj test suite. However, not all best known Pareto set approximations are entirely contained within [−5, 5] n .

Published
2016

4. Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed

Author

Tobias Wagner, Dimo Brockhoff, Dejan Tušar, Tea Tušar, Anne Auger, Nikolaus Hansen, Machine Learning and Optimisation (TAO), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dortmund [Dortmund] (TU), ANR-12-MONU-0009,NumBBO,Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire(2012), Machine Learning and Optimisation ( TAO ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Parallel Cooperative Multi-criteria Optimization ( DOLPHIN ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Technische Universität Dortmund [Dortmund] ( TU ), ANR : ANR-2012-MONU-0009,NumBBO, Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, and Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC], 021103 operations research, Computer science, Testbed, 0211 other engineering and technologies, Sampling (statistics), 02 engineering and technology, Benchmarking, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], computer.software_genre, Computational science, Random search, Dimension (vector space), Black-box optimization, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), Test suite, 020201 artificial intelligence & image processing, Data mining, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Bi-objective optimization, computer
Abstract

International audience; The Comparing Continuous Optimizers platform COCO has become a standard for benchmarking numerical (single-objective) optimization algorithms effortlessly. In 2016, COCO has been extended towards multi-objective optimization by providing a first bi-objective test suite. To provide a baseline, we benchmark a pure random search on this bi-objective bbob-biobj test suite of the COCO platform. For each combination of function, dimension n, and instance of the test suite, $10^6 · n$ candidate solutions are sampled uniformly within the sampling box $[−5, 5]^n$ .

Published
2016

5. The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite

Author

Tobias Wagner, Dejan Tušar, Tea Tušar, Dimo Brockhoff, Nikolaus Hansen, Anne Auger, Machine Learning and Optimisation (TAO), Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dortmund [Dortmund] (TU), ANR-12-MONU-0009,NumBBO,Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire(2012), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Machine Learning and Optimisation ( TAO ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Parallel Cooperative Multi-criteria Optimization ( DOLPHIN ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Technische Universität Dortmund [Dortmund] ( TU ), and ANR : ANR-2012-MONU-0009,NumBBO

Subjects
[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC], Mathematical optimization, 021103 operations research, Crossover, 0211 other engineering and technologies, Binary number, 02 engineering and technology, Variation (game tree), [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Benchmarking, Operator (computer programming), Differential evolution, Black-box optimization, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), Test suite, 020201 artificial intelligence & image processing, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Bi-objective optimization, Algorithm, Selection (genetic algorithm), Mathematics
Abstract

International audience; The S-metric-Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) is one of the best-known indicator-based multi-objective optimization algorithms. It employs the S-metric or hypervolume indicator in its (steady-state) selection by deleting in each iteration the solution that has the smallest contribution to the hypervolume indicator. In the SMS-EMOA, the conceptual idea is this hypervolume-based selection. Hence the algorithm can, for example, be combined with several variation operators. Here, we benchmark two versions of SMS-EMOA which employ differential evolution (DE) and simulated binary crossover (SBX) with polynomial mutation (PM) respectively on the newly introduced bi-objective bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. The results un-surprisingly reveal that the choice of the variation operator is crucial for performance with a clear advantage of the DE variant on almost all functions.

Published
2016

6. Benchmarking RM-MEDA on the Bi-objective BBOB-2016 Test Suite

Author

Nikolaus Hansen, Tea Tušar, Anne Auger, Tobias Wagner, Dimo Brockhoff, Dejan Tušar, Machine Learning and Optimisation (TAO), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dortmund [Dortmund] (TU), ANR-12-MONU-0009,NumBBO,Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire(2012), Machine Learning and Optimisation ( TAO ), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ) -Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique ( Inria ), Parallel Cooperative Multi-criteria Optimization ( DOLPHIN ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 ( CRIStAL ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ) -Ecole Centrale de Lille-Institut Mines-Télécom [Paris]-Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Technische Universität Dortmund [Dortmund] ( TU ), ANR : ANR-2012-MONU-0009,NumBBO, Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, and Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
[ MATH.MATH-OC ] Mathematics [math]/Optimization and Control [math.OC], Mathematical optimization, Computer science, 0102 computer and information sciences, 02 engineering and technology, Function (mathematics), Benchmarking, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 01 natural sciences, chemistry.chemical_compound, chemistry, Estimation of distribution algorithm, Dimension (vector space), 010201 computation theory & mathematics, Black-box optimization, 0202 electrical engineering, electronic engineering, information engineering, Bi objective, Benchmark (computing), Test suite, 020201 artificial intelligence & image processing, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Bi-objective optimization, MEDA
Abstract

International audience; In this paper, we benchmark the Regularity Model-Based Multiobjective Estimation of Distribution Algorithm (RM-MEDA) of Zhang et al. on the bi-objective bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. It turns out that, starting from about 200 times dimension many function evaluations, RM-MEDA shows a linear increase in the solved hypervolume-based target values with time until a stagnation of the performance occurs rather quickly on all problems. The final percentage of solved hy-pervolume targets seems to decrease with the problem dimension .

Published
2016

7. R2 Indicator Based Multiobjective Search

Author

Dimo Brockhoff, Heike Trautmann, Tobias Wagner, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Technische Universität Dortmund [Dortmund] (TU), Westfälische Wilhelms-Universität Münster (WWU), ANR NumBBO, ANR-12-MONU-0009,NumBBO,Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire(2012), and Westfälische Wilhelms-Universität Münster = University of Münster (WWU)

Subjects
R2-EMOA, Mathematical optimization, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], computer.software_genre, Multi-objective optimization, Multiobjective optimization algorithm, Set (abstract data type), Computational Mathematics, Distribution (mathematics), Empirical research, indicator-based search, Performance comparison, environmental selection, Computer Simulation, Performance indicator, Data mining, performance assessment, R2 indicator, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], computer, Algorithms, Mathematics, Multiobjective optimization
Abstract

In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The [Formula: see text] and the hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the [Formula: see text] indicator exist. In this extended version of our previous conference paper, we thus perform a comprehensive investigation of the properties of the [Formula: see text] indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of [Formula: see text] solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the [Formula: see text] and HV indicator are presented. Furthermore, the [Formula: see text] indicator is integrated into an indicator-based steady-state evolutionary multiobjective optimization algorithm (EMOA). It is shown that the so-called [Formula: see text]-EMOA can accurately approximate the optimal distribution of [Formula: see text] solutions regarding [Formula: see text].

Published
2015

8. A benchmark of kriging-based infill criteria for noisy optimization

Author

Tobias Wagner, David Ginsbourger, Victor Picheny, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Technische Universität Dortmund [Dortmund] (TU), University of Bern, Deutsche Forschungsgemeinschaft (DFG) [SFB/TR TRR 30] Funding Text : The contributions of Tobias Wagner to this paper are based on investigations of the project D5 of the Collaborative Research Center SFB/TR TRR 30, which is kindly supported by the Deutsche Forschungsgemeinschaft (DFG)., and Picheny, Victor

Subjects
Mathematical optimization, Control and Optimization, Covariance function, 0211 other engineering and technologies, 02 engineering and technology, Metamodeling, symbols.namesake, 510 Mathematics, DESIGN, Kriging, Homoscedasticity, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], [MATH]Mathematics [math], Gaussian process, GLOBAL OPTIMIZATION, Mathematics, EGO, 021103 operations research, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Covariance, Computer Graphics and Computer-Aided Design, Computer Science Applications, COMPUTER EXPERIMENTS, Control and Systems Engineering, Gaussian noise, SIMULATION, symbols, 020201 artificial intelligence & image processing, Engineering design process, Noise, Software
Abstract

International audience; Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.

Published
2013

9. Preference Articulation by Means of the R2 Indicator

Author

Dimo Brockhoff, Heike Trautmann, Tobias Wagner, Technische Universität Dortmund [Dortmund] (TU), Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Mathematical optimization, 021103 operations research, media_common.quotation_subject, 0211 other engineering and technologies, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Multi-objective optimization, Domain (software engineering), Set (abstract data type), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Quality (business), Weight, Performance indicator, Empirical evidence, Preference (economics), media_common, Mathematics
Abstract

International audience; In multi-objective optimization, set-based performance indicators have become the state of the art for assessing the quality of Pareto front approximations. As a consequence, they are also more and more used within the design of multi-objective optimization algorithms. The R2 and the Hypervolume (HV) indicator represent two popular examples. In order to understand the behavior and the approximations preferred by these indicators and algorithms, a comprehensive knowledge of the indicator's properties is required. Whereas this knowledge is available for the HV, we presented a first approach in this direction for the R2 indicator just recently. In this paper, we build upon this knowledge and enhance the considerations with respect to the integration of preferences into the R2 indicator. More specifically, we analyze the effect of the reference point, the domain of the weights, and the distribution of weight vectors on the optimization of μ solutions with respect to the R2 indicator. By means of theoretical findings and empirical evidence, we show the potentials of these three possibilities using the optimal distribution of μ solutions for exemplary setups.

Published
2013

10. R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection

Author

Tobias Wagner, Dimo Brockhoff, Heike Trautmann, Statistics Department, Technische Universität Dortmund [Dortmund] (TU), Institute of Machining Technology, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), and ANR-12-MONU-0009,NumBBO,Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire(2012)

Subjects
0209 industrial biotechnology, Mathematical optimization, Unary operation, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Multi-objective optimization, Multiobjective optimization algorithm, Multiobjective optimization problem, 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Weight, Selection criterion, Selection (genetic algorithm), Mathematics
Abstract

short paper; International audience; An indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers' preferences can be included by adjusting the weight vector distributions of the indicator which results in a focused search behavior.

Published
2013

11. On the Properties of the R2 Indicator

Author

Tobias Wagner, Heike Trautmann, Dimo Brockhoff, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Institute of Machining Technology, Technische Universität Dortmund [Dortmund] (TU), and Statistics Department

Subjects
Mathematical optimization, 021103 operations research, 0211 other engineering and technologies, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Multi-objective optimization, Set (abstract data type), Distribution (mathematics), Empirical research, Performance comparison, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Performance indicator, Mathematics
Abstract

International audience; In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The R2 and the Hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the R2 indicator exist. In this paper, we thus perform a comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of μ solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the R2 and HV indicator are presented.

Published
2012

Catalog

Books, media, physical & digital resources
See catalog results