Dimo Brockhoff

Publications

Copyright Notice: Note that most of the papers listed below have been published and the copyrights have been transferred to the respective publishers. Therefore, not all papers can be provided for download directly but you can find author versions and links to the original papers wherever possible.

My Google Scholar page can be found here.

Edited Books

[1 — benp2023a]

D. Brockhoff, M. Emmerich, B. Naujoks, and R. Purshouse. Many-criteria Optimization and Decision Analysis: State-of-the-art, Present Challenges, and Future Perspectives. Springer, 2023. (webpage)

Peer-reviewed Journal Papers

[10 — baht2022a]

D. Brockhoff, A. Auger, N. Hansen, and T. Tušar. Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites . Evolutionary Computation, 30(2), 165–193, 2022. (doi)

[9 — habt2022a]

N. Hansen, A. Auger, D. Brockhoff and T. Tušar. Anytime Performance Assessment in Blackbox Optimization Benchmarking. IEEE Transactions on Evolutionary Computing, 26(6), 1293–1305, 2022. (doi)

[8 — harm2021a]

N. Hansen, A. Auger, R. Ros, O. Mersmann, T. Tušar, and D. Brockhoff. COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting. Optimization Methods and Software, 36(1), 114–144, 2021. (doi)

[7 — vabh2021a]

K. Varelas, O. Ait El Hara, D. Brockhoff, N. Hansen, D. M. Nguyen, T. Tušar, and A. Auger. Benchmarking large-scale continuous optimizers: The bbob-largescale testbed, a COCO software guide and beyond. Applied Soft Computing, 97(A), 106737, 2021. (doi)

[6 — bwt2015a]

D. Brockhoff, T. Wagner, and, H. Trautmann. R2 Indicator Based Multiobjective Search. Evolutionary Computation, 23(3):369–395, 2015. (PDF) (doi) (bibtex) (suppl. material)

[5 — bbtz2013a]

D. Brockhoff, J. Bader, L. Thiele, and E. Zitzler. Directed Multiobjective Optimization Based on the Hypervolume Indicator. Journal of Multi-Criteria Decision Analysis, 20(5-6):291–317, 2013. (doi) (bibtex) (suppl. material)

[4 — abbz2012a]

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler. Hypervolume-based Multiobjective Optimization: Theoretical Foundations and Practical Implications. Theoretical Computer Science, 425:75–103, 2012. (PDF) (doi) (bibtex)

[3 — bfhk2009a]

D. Brockhoff, T. Friedrich, N. Hebbinghaus, C. Klein, F. Neumann, and E. Zitzler. On the Effects of Adding Objectives to Plateau Functions. IEEE Transactions on Evolutionary Computation, 13(3):591–603, 2009. (PDF) (doi) (bibtex)

[2 — bz2009c]

D. Brockhoff and E. Zitzler. Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications. Evolutionary Computation, 17(2):135–166, 2009. (PDF) (doi) (bibtex) (online access) (suppl. material)

[1 — tjcn2007a]

M. Terzer, M. Jovanovic, A. Choutko, O. Nikolayeva, A. Korn, D. Brockhoff, F. Zürcher, M. Friedmann, R. Schütz, E. Zitzler, J. Stelling, and S. Panke.

Design of a biological half adder. IET Synthetic Biology, 1(1–2):53–58, 2007. (PDF) (bibtex) (doi)

Peer-reviewed Conference Papers

[38 — ghbl2023a]

M. Gharafi, N. Hansen, D. Brockhoff, and R. Le Riche. Multiobjective optimization with a quadratic surrogate-assisted CMA-ES. In Genetic and Evolutionary Computation Conference (GECCO 2023), pages 652–660. ACM, 2023. (doi)

[37 — tbj2019a]

T. Tušar, D. Brockhoff, and N. Hansen. Mixed-integer benchmark problems for single- and bi-objective optimization. In Genetic and Evolutionary Computation Conference (GECCO 2019), pages 718–726. ACM, 2019. (pdf) (hal) (doi)

[36 — thab2019a]

C. Touré, N. Hansen, A. Auger, and D. Brockhoff. Uncrowded hypervolume improvement: COMO-CMA-ES and the sofomore framework. In Genetic and Evolutionary Computation Conference (GECCO 2019), pages 638–646. ACM, 2019. (pdf) (arXiv) (doi)

[35 — tvbh2019a]

T. Tušar, V. Volz, N. Hansen, and D. Brockhoff. Handling real-world problems within the COCO platform. In International Multiconference Information Society (IS 2019), pages 37–40. 2019

[34 — vabh2018a]

K. Varelas, A. Auger, D. Brockhoff, N. Hansen, O. Ait ElHara, Y. Semet, R. Kassab, and F. Barbaresco. A Comparative Study of Large-Scale Variants of CMA-ES. In Parallel Problem Solving from Nature (PPSN 2018), pages 3–15. Springer, 2018. (pdf) (bibtex) (hal)

[33 — thb2017a]

T. Tušar, N. Hansen, and D. Brockhoff. Anytime Benchmarking of Budget-Dependent Algorithms with the COCO Platform. In International Multiconference Information Society (IS 2017), pages 47–50. 2017 (pdf) (bibtex) (hal)

[32 — baht2017a]

D. Brockhoff, A. Auger, N. Hansen, and T. Tušar. Quantitative Performance Assessment of Multiobjective Optimizers: The Average Runtime Attainment Function. In Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), pages 103–119. Springer, 2017 (pdf) (bibtex) (hal) (doi)

[31 — bth2015a]

D. Brockhoff, T.-D. Tran, and N. Hansen. Benchmarking Numerical Multiobjective Optimizers Revisited. In Genetic and Evolutionary Computation Conference (GECCO 2015), pages 639–646. ACM, 2015. (pdf) (bibtex) (hal) (doi) (suppl. material)

[30 — zdlb2015a]

S. Zapotecas-Martínez, B. Derbel, A. Liefooghe, D. Brockhoff, H. Aguirre, and K. Tanaka. Injecting CMA-ES into MOEA/D. In Genetic and Evolutionary Computation Conference (GECCO 2015), pages 783–790. ACM, 2015. (pdf) (bibtex) (hal) (doi)

[29 — broc2015b]

D. Brockhoff. Comparison of the MATSuMoTo Library for Expensive Optimization on the Noiseless Black-Box Optimization Benchmarking Testbed. In Congress on Evolutionary Computation, pages 2026–2033. IEEE, 2015. (pdf) (bibtex) (hal) (doi)

[28 — broc2015a]

D. Brockhoff. A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment. In Conference on Evolutionary Multi-Criterion Optimization (EMO 2015), pages 187–201. Springer, 2015. (pdf) (doi) (bibtex) (hal)

[27 — dblv2014b]

B. Derbel, D. Brockhoff, A. Liefooghe, and S. Verel. On the Impact of Multiobjective Scalarizing Functions. In Parallel Problem Solving from Nature (PPSN 2014), pages 548–558, Springer, 2014. (PDF) (doi) (bibtex)

[26 — bhk2014a]

D. Brockhoff, Y. Hamadi, and S. Kaci. Using Comparative Preference Statements in Hypervolume-Based Interactive Multiobjective Optimization. In Learning and Intelligent Optimization (LION 2014), pages 121–136, Springer, 2014. (PDF) (bibtex) (suppl. material)

[25 — dbl2013a]

B. Derbel, D. Brockhoff, and A. Liefooghe. Force-based Cooperative Search Directions in Evolutionary Multi-objective Optimization. In Conference on Evolutionary Multi-Criterion Optimization (EMO 2013), pages 383–397, Springer, 2013. (PDF) (doi) (bibtex)

[24 — wtb2013a]

T. Wagner, H. Trautmann, and D. Brockhoff. Preference Articulation by Means of the R2 Indicator. In Conference on Evolutionary Multi-Criterion Optimization (EMO 2013), pages 81–95, Springer, 2013. (PDF) (doi) (bibtex) (suppl. material)

[23 — twb2013a]

H. Trautmann, T. Wagner, and D. Brockhoff. R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In Learning and Intelligent Optimization Conference (LION 2013), pages 70–74, Springer, 2013. Short paper. (PDF) (doi) (bibtex) (suppl. material)

[22 — blnr2012a]

D. Brockhoff, M. López-Ibáñez, B. Naujoks, and G. Rudolph. Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms. In Parallel Problem Solving from Nature (PPSN 2012), pages 123–132. Springer, 2012. (PDF) (doi) (bibtex)

[21 — bwt2012a]

D. Brockhoff, T. Wagner, and H. Trautmann. On the Properties of the R2 Indicator. In Genetic and Evolutionary Computation Conference (GECCO 2012), pages 465–472. ACM, 2012. Best Paper Award in EMO Track. (PDF) (doi) (bibtex) (suppl. material)

[20 — abh2011b]

A. Auger, D. Brockhoff, and N. Hansen. Mirrored Sampling in Evolution Strategies With Weighted Recombination. In Genetic and Evolutionary Computation Conference (GECCO 2011), pages 861–868. ACM, 2011. (PDF) (doi) (bibtex) (suppl. material)

[19 — abh2011a]

A. Auger, D. Brockhoff, and N. Hansen. Analyzing the Impact of Mirrored Sampling and Sequential Selection in Elitist Evolution Strategies. In Foundations of Genetic Algorithms (FOGA 2011), pages 127–138. ACM, 2011. (PDF) (doi) (bibtex) (suppl. material)

[18 — broc2010b]

D. Brockhoff. Optimal μ-Distributions for the Hypervolume Indicator for Problems With Linear Bi-Objective Fronts: Exact and Exhaustive Results. In K. Deb et al., editors, Simulated Evolution and Learning (SEAL 2010), volume 6457 of LNCS, pages 24–34. Springer, 2010. (PDF) (doi) (bibtex)

[17 — abb2010a]

A. Auger, J. Bader, and D. Brockhoff. Theoretically Investigating Optimal μ-Distributions for the Hypervolume Indicator: First Results For Three Objectives. In R. Schaefer et al., editors, Conference on Parallel Problem Solving from Nature (PPSN XI), volume 6238 of LNCS, pages 586–596. Springer, 2010. (PDF) (doi) (bibtex)

[16 — baha2010a]

D. Brockhoff, A. Auger, N. Hansen, D. V. Arnold, and T. Hohm. Mirrored Sampling and Sequential Selection for Evolution Strategies. In R. Schaefer et al., editors, Conference on Parallel Problem Solving from Nature (PPSN XI), volume 6238 of LNCS, pages 11–21. Springer, 2010. (PDF) (doi) (bibtex) (suppl. material)

[15 — bz2010a]

D. Brockhoff and E. Zitzler. Automated Aggregation and Omission of Objectives to Handle Many-Objective Problems. In Conference on Multiple Objective and Goal Programming (MOPGP 2008), Lecture Notes in Economics and Mathematical Systems, pages 81–102. Springer, 2010. (doi) (bibtex) (online access)

[14 — wbhb2009a]

M. Woehrle, D. Brockhoff, T. Hohm, and S. Bleuler. Investigating Coverage and Connectivity Trade-offs in Wireless Sensor Networks: The Benefits of MOEAs. In M. Ehrgott et al., editors, Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems; Proceedings of the Multiple Criteria Decision Making Conference (MCDM 2008), volume 634 of Lecture Notes in Economics and Mathematical Systems, pages 211–221. Springer, 2009. (doi) (bibtex) (online access)

[13 — abbz2009b]

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler. Articulating User Preferences in Many-Objective Problems by Sampling the Weighted Hypervolume. In G. Raidl et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2009), pages 555–562, New York, NY, USA, 2009. ACM. (PDF) (bibtex) (doi)

[12 — abbz2009c]

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler. Investigating and Exploiting the Bias of the Weighted Hypervolume to Articulate User Preferences. In G. Raidl et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2009), pages 563–570, New York, NY, USA, 2009. ACM. (PDF) (bibtex) (doi)

[11 — bbwz2009a]

J. Bader, D. Brockhoff, S. Welten, and E. Zitzler. On Using Populations of Sets in Multiobjective Optimization. In M. Ehrgott et al., editors, Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), volume 5467 of LNCS, pages 140–154. Springer, 2009. (PDF) (doi) (bibtex) (online access)

[10 — abbz2009a]

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler. Theory of the Hypervolume Indicator: Optimal μ-Distributions and the Choice of the Reference Point. In Foundations of Genetic Algorithms (FOGA 2009), pages 87–102, New York, NY, USA, 2009. ACM. (PDF) (bibtex) (doi) (suppl. material)

[9 — bfn2008a]

D. Brockhoff, T. Friedrich, and F. Neumann. Analyzing Hypervolume Indicator Based Algorithms. In G. Rudolph et al., editors, Conference on Parallel Problem Solving From Nature (PPSN X), volume 5199 of LNCS, pages 651–660. Springer, 2008. (bibtex) (online access)

[8 — ubz2008a]

T. Ulrich, D. Brockhoff, and E. Zitzler. Pattern Identification in Pareto-Set Approximations. In M. Keijzer et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2008), pages 737–744. ACM, 2008. (bibtex) (doi)

[7 — bz2007c]

D. Brockhoff and E. Zitzler. Improving Hypervolume-based Multiobjective Evolutionary Algorithms by Using Objective Reduction Methods. In Congress on Evolutionary Computation (CEC 2007), pages 2086–2093. IEEE Press, 2007. (doi) (bibtex) (online access) (suppl. material)

[6 — bfhk2007a]

D. Brockhoff, T. Friedrich, N. Hebbinghaus, C. Klein, F. Neumann, and E. Zitzler. Do Additional Objectives Make a Problem Harder?. In D. Thierens et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2007), pages 765–772, New York, NY, USA, 2007. ACM Press. (bibtex) (online access)

[5 — bz2007d]

D. Brockhoff and E. Zitzler. Dimensionality Reduction in Multiobjective Optimization: The Minimum Objective Subset Problem. In K. H. Waldmann and U. M. Stocker, editors, Operations Research Proceedings 2006, pages 423–429. Springer, 2007. (bibtex) (online access) (suppl. material)

[4 — zbt2007a]

E. Zitzler, D. Brockhoff, and L. Thiele. The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration. In S. Obayashi et al., editors, Conference on Evolutionary Multi-Criterion Optimization (EMO 2007), volume 4403 of LNCS, pages 862–876, Berlin, 2007. Springer. (bibtex) (doi) (suppl. material)

[3 — bz2006d]

D. Brockhoff and E. Zitzler. Are All Objectives Necessary? On Dimensionality Reduction in Evolutionary Multiobjective Optimization. In T. P. Runarsson et al., editors, Conference on Parallel Problem Solving from Nature (PPSN IX), volume 4193 of LNCS, pages 533–542, Berlin, Germany, 2006. Springer. (bibtex) (online access) (suppl. material)

[2 — bbde2004a]

P. Briest, D. Brockhoff, B. Degener, M. Englert, C. Gunia, O. Heering, T. Jansen, M. Leifhelm, K. Plociennik, H. Röglin, A. Schweer, D. Sudholt, S. Tannenbaum, and I. Wegener. Experimental Supplements to the Theoretical Analysis of EAs on Problems from Combinatorial Optimization. In X. Yao et al., editors, Conference on Parallel Problem Solving from Nature (PPSN VIII), volume 3242 of LNCS, pages 21–30, Berlin, Germany, 2004. Springer. (bibtex) (online access)

[1 — bbde2004b]

P. Briest, D. Brockhoff, B. Degener, M. Englert, C. Gunia, O. Heering, T. Jansen, M. Leifhelm, K. Plociennik, H. Röglin, A. Schweer, D. Sudholt, S. Tannenbaum, and I. Wegener. The Ising Model: Simple Evolutionary Algorithms as Adaptation Schemes. In X. Yao et al., editors, Conference on Parallel Problem Solving from Nature (PPSN VIII), volume 3242 of LNCS, pages 31–40, Berlin, Germany, 2004. Springer. (bibtex) (online access)

Technical Reports and Preprints

[17 — bttw2016a]

D. Brockhoff, T. Tušar, D. Tušar, T. Wagner, N. Hansen, and A. Auger. Biobjective Performance Assessment with the COCO Platform. CoRR abs/1605.01746, 2016. (PDF) (bibtex) (arXiv)

[16 — habt2016a]

N. Hansen, A. Auger, D. Brockhoff, D. Tušar, and T. Tušar. COCO: Performance Assessment. CoRR abs/1605.03560, 2016. (PDF) (bibtex) (arXiv)

[15 — hamt2016a]

N. Hansen, A. Auger, O. Mersmann, T. Tušar, and D. Brockhoff. COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting. CoRR abs/1603.08785, 2016. (PDF) (bibtex) (arXiv)

[14 — htma2016a]

N. Hansen, T. Tušar, O. Mersmann, A. Auger, and D. Brockhoff. COCO: The Experimental Procedure. CoRR abs/1603.08776, 2016. (PDF) (bibtex) (arXiv)

[13 — tbha2016a]

T. Tušar, D. Brockhoff, N. Hansen, and A. Auger. COCO: The Bi-objective Black Box Optimization Benchmarking (bbob-biobj) Test Suite. CoRR abs/1604.00359, 2016. (PDF) (bibtex) (arXiv)

[12 — dblv2014a]

D. Brockhoff, B. Derbel, A. Liefooghe, and S. Verel. On the Impact of Scalarizing Functions on Evolutionary Multiobjective Optimization. Rapport de Recherche RR-8512, INRIA Lille - Nord Europe, 2014. (PDF) (bibtex) (online access)

[11 — bhk2012a]

D. Brockhoff, Y. Hamadi, and S. Kaci. Interactive Optimization With Weighted Hypervolume Based EMO Algorithms: Preliminary Experiments. Rapport de Recherche RR-8103, INRIA Lille - Nord Europe, 2012. (PDF) (bibtex) (online access) (suppl. material)

[10 — bb2010a]

N. Beume and D. Brockhoff. Summary of the First GECCO Workshop on Theoretical Aspects of Evolutionary Multiobjective Optimization. Rapport de Recherche RR-7444, INRIA Saclay—Île-de-France, 2010. (PDF) (bibtex) (online access)

[9 — bam2010a]

D. Brockhoff, A. Auger, and F. Marchal. Calibrating Traffic Simulations as an Application of CMA-ES in Continuous Blackbox Optimization: First Results. Rapport de Recherche RR-7304, INRIA Saclay—Île-de-France, June 2010. (PDF) (bibtex) (online access)

[8 — abh2010l]

A. Auger, D. Brockhoff, and N. Hansen. Mirrored Sampling and Sequential Selection for Evolution Strategies. Rapport de Recherche RR-7249, INRIA Saclay—Île-de-France, June 2010. (PDF) (bibtex) (online access)

[7 — broc2009c]

D. Brockhoff. Theoretical Aspects of Evolutionary Multiobjective Optimization—A Review. Rapport de Recherche RR-7030, INRIA Saclay—Île-de-France, September 2009. (PDF) (bibtex) (online access)

[6 — wbhb2008b]

M. Woehrle, D. Brockhoff, T. Hohm, and S. Bleuler. Investigating Coverage and Connectivity Trade-offs in Wireless Sensor Networks: The Benefits of MOEAs. TIK Report 294, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, October 2008. (PDF) (bibtex)

[5 — wbh2007a]

M. Woehrle, D. Brockhoff, and T. Hohm. A New Model for Deployment Coverage and Connectivity of Wireless Sensor Networks. TIK-Report 278, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, September 2007. (PDF) (bibtex)

[4 — bz2007a]

D. Brockhoff and E. Zitzler. Offline and Online Objective Reduction in Evolutionary Multiobjective Optimization Based on Objective Conflicts. TIK Report 269, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, April 2007. (PDF) (bibtex) (suppl. material)

[3 — bz2006c]

D. Brockhoff and E. Zitzler. Dimensionality Reduction in Multiobjective Optimization with (Partial) Dominance Structure Preservation: Generalized Minimum Objective Subset Problems. TIK Report 247, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, April 2006. (PDF) (bibtex) (suppl. material)

[2 — bz2006a]

D. Brockhoff and E. Zitzler. On Objective Conflicts and Objective Reduction in Multiple Criteria Optimization. TIK Report 243, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, February 2006. (PDF) (bibtex) (suppl. material)

[1 — bzfw2006a]

S. Bleuler, P. Zimmermann, M. Friberg, A. Wille, S. Barkow, D. Brockhoff, D. Schöner, L. Hennig, P. Bühlmann, W. Gruissem, L. Thiele, and E. Zitzler. Cluster Analysis of Multiple Time Course Data Sets. TIK Report 241, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, 2006. (bibtex)

Bookchapters and Theses

[5 — benp2023b]

D. Brockhoff, M. Emmerich, B. Naujoks, and R. Purshouse. Introduction to Many-Criteria Optimization and Decision Analysis. In D. Brockhoff et al. editors, Many-Criteria Optimization and Decision Analysis State-of-the-Art, Present Challenges, and Future Perspectives, pages 3–28. Springer, 2023. (webpage) (bibtex)

[4 — broc2011a]

D. Brockhoff. Theoretical Aspects of Evolutionary Multiobjective Optimization. In A. Auger and B. Doerr, editors, Theory of Randomized Search Heuristics: Foundations and Recent Developments, pages 101–139. World Scientific Publishing, 2011. (online access) (bibtex)

[3 — broc2009b]

D. Brockhoff. Many-Objective Optimization and Hypervolume Based Search. Shaker Verlag, Aachen, Germany, 2009. PhD thesis at ETH Zurich. (PDF) (bibtex) (online access)

[2 — bsdz2007a]

D. Brockhoff, D. K. Saxena, K. Deb, and E. Zitzler. On Handling a Large Number of Objectives A Posteriori and During Optimization. In J. Knowles, D. Corne, and K. Deb, editors, Multiobjective Problem Solving from Nature: From Concepts to Applications, pages 377–403. Springer, 2007. (doi) (bibtex) (online access) (suppl. material)

[1 — broc2005a]

D. Brockhoff. Randomisierte Suchheuristiken für das Graphfärbungsproblem (in German). Master's thesis, Universität Dortmund, 2005. (bibtex)

Workshop Papers

[29 — broc2023a]

D. Brockhoff. Comparing Boundary Handling Techniques of CMA-ES on the bbob and sbox-cost Test Suites. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2023). ACM, 2023. (DOI) (bibtex)

[28 — bchw2023a]

D. Brockhoff, P. Capetillo, J. Hornewall and R. Walker. Benchmarking the Borg algorithm on the Biobjective bbob-biobj Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2023). ACM, 2023. (DOI) (bibtex)

[27 — bpah2021a]

D. Brockhoff, B. Plaquevent-Jourdain, A. Auger, and N. Hansen. DMS and MultiGLODS: Black-box Optimization Benchmarking of Two Direct Search Methods on the bbob-biobj Test Suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2021). ACM, 2021. (DOI) (bibtex)

[26 — bt2019a]

D. Brockhoff and T. Tušar. Benchmarking Algorithms from the Platypus Framework on the Biobjective bbob-biobj Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2019). ACM, 2019. (DOI) (bibtex)

[25 — bh2019a]

D. Brockhoff and N. Hansen. The Impact of Sample Volume in Random Search on the bbob Test Suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2019). ACM, 2019. (DOI) (bibtex)

[24 — bfab2018a]

A. Blelly, M. Felipe-Gomez, A. Auger, and D. Brockhoff. Stopping Criteria, Initialization, and Implementations of BFGS and their Effect on the BBOB Test Suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2018). ACM, 2018. (PDF) (bibtex)

[23 — abht2016a]

A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, and T. Wagner. Benchmarking MATLAB's gamultiobj (NSGA-II) on the Bi-objective BBOB-2016 Test Suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2016), pages 1233–1239. ACM, 2016. (PDF) (bibtex) (doi)

[22 — abht2016b]

A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, and T. Wagner. Benchmarking the Pure Random Search on the Bi-Objective BBOB-2016 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2016), pages 1217–1223. ACM, 2016. (PDF) (bibtex) (doi)

[21 — abht2016c]

A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, and T. Wagner. The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2016), pages 1225–1232. ACM, 2016. (PDF) (bibtex) (doi)

[20 — abht2016d]

A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, and T. Wagner. The Impact of Search Volume on the Performance of RANDOMSEARCH on the Bi-objective BBOB-2016 Test Suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2016), pages 1257–1264. ACM, 2016. (PDF) (bibtex) (doi)

[19 — abht2016e]

A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, and T. Wagner. Benchmarking RM-MEDA on the Bi-objective BBOB-2016 Test Suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2016), pages 1241–1247. ACM, 2016. (PDF) (bibtex) (doi)

[18 — bbw2015a]

D. Brockhoff, B. Bischl, and T. Wagner. The Impact of Initial Designs on the Performance of MATSuMoTo on the Noiseless BBOB-2015 Testbed: A Preliminary Study. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2015), pages 1159–1166. ACM, 2015. (PDF) (bibtex) (doi)

[17 — abh2013a]

A. Auger, D. Brockhoff, and N. Hansen. Benchmarking the Local Metamodel CMA-ES on the Noiseless BBOB'2013 Test Bed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2013), pages 1225–1232. ACM, 2013. (PDF) (bibtex) (doi)

[16 — tbd2013a]

T.-D. Tran, D. Brockhoff, and B. Derbel. Multiobjectivization with NSGA-II on the Noiseless BBOB Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2013), pages 1217–1224. ACM, 2013. (PDF) (bibtex) (doi)

[15 — bah2012a]

D. Brockhoff, A. Auger, and N. Hansen. On the Effect of Mirroring in the IPOP Active CMA-ES on the Noiseless BBOB Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2012), pages 277–284. ACM, 2012. (PDF) (bibtex) (doi) (suppl. material)

[14 — bah2012b]

D. Brockhoff, A. Auger, and N. Hansen. Comparing Mirrored Mutations and Active Covariance Matrix Adaptation in the IPOP-CMA-ES on the Noiseless BBOB Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2012), pages 297–303. ACM, 2012. (PDF) (bibtex) (doi) (suppl. material)

[13 — bah2012c]

D. Brockhoff, A. Auger, and N. Hansen. On the Impact of a Small Initial Population Size in the IPOP Active CMA-ES with Mirrored Mutations on the Noiseless BBOB Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2012), pages 285–290. ACM, 2012. (PDF) (bibtex) (doi) (suppl. material)

[12 — bah2012d]

D. Brockhoff, A. Auger, and N. Hansen. On the Impact of Active Covariance Matrix Adaptation in the CMA-ES With Mirrored Mutations and Small Initial Population Size on the Noiseless BBOB Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2012), pages 291–296. ACM, 2012. (PDF) (bibtex) (doi) (suppl. material)

[11 — abh2010k]

A. Auger, D. Brockhoff, and N. Hansen. Benchmarking the (1,4)-CMA-ES With Mirrored Sampling and Sequential Selection on the Noisy BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1625–1632. ACM, 2010. (PDF) (bibtex) (online access)

[10 — abh2010j]

A. Auger, D. Brockhoff, and N. Hansen. Benchmarking the (1,4)-CMA-ES With Mirrored Sampling and Sequential Selection on the Noiseless BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1617–1624. ACM, 2010. (PDF) (bibtex) (online access)

[9 — abh2010i]

A. Auger, D. Brockhoff, and N. Hansen. Mirrored Variants of the (1,4)-CMA-ES Compared on the Noisy BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1583–1590. ACM, 2010. (PDF) (bibtex) (online access)

[8 — abh2010h]

A. Auger, D. Brockhoff, and N. Hansen. Mirrored Variants of the (1,4)-CMA-ES Compared on the Noiseless BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1559–1566. ACM, 2010. (PDF) (bibtex) (online access)

[7 — abh2010g]

A. Auger, D. Brockhoff, and N. Hansen. Mirrored Variants of the (1,2)-CMA-ES Compared on the Noisy BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1575–1582. ACM, 2010. (PDF) (bibtex) (online access)

[6 — abh2010f]

A. Auger, D. Brockhoff, and N. Hansen. Mirrored Variants of the (1,2)-CMA-ES Compared on the Noiseless BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1551–1558. ACM, 2010. (PDF) (bibtex) (online access)

[5 — abh2010e]

A. Auger, D. Brockhoff, and N. Hansen. Investigating the Impact of Sequential Selection in the (1,4)-CMA-ES on the Noisy BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1611–1616. ACM, 2010. (PDF) (bibtex) (online access)

[4 — abh2010d]

A. Auger, D. Brockhoff, and N. Hansen. Investigating the Impact of Sequential Selection in the (1,4)-CMA-ES on the Noiseless BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1597–1604. ACM, 2010. (PDF) (bibtex) (online access)

[3 — abh2010c]

A. Auger, D. Brockhoff, and N. Hansen. Investigating the Impact of Sequential Selection in the (1,2)-CMA-ES on the Noisy BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1605–1610. ACM, 2010. (PDF) (bibtex) (online access)

[2 — abh2010b]

A. Auger, D. Brockhoff, and N. Hansen. Investigating the Impact of Sequential Selection in the (1,2)-CMA-ES on the Noiseless BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1591–1596. ACM, 2010. (PDF) (bibtex) (online access)

[1 — abh2010a]

A. Auger, D. Brockhoff, and N. Hansen. Comparing the (1+1)-CMA-ES with a Mirrored (1+2)-CMA-ES with Sequential Selection on the Noiseless BBOB-2010 Testbed. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2010), pages 1543–1550. ACM, 2010. (PDF) (bibtex) (online access)

Last updated: Sat, 17 Aug 2024 09:31