Dimo Brockhoff

Peer-Reviewed Publications Sorted By Publication Year

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.

2023

[1 — 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)

2022

[2 — 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)

[1 — 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)

2021

[2 — 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)

[1 — 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.

2019

[3 — 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)

[2 — 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)

[1 — 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

2018

[1 — 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)

2017

[2 — 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. ACM, 2017 (pdf) (bibtex) (hal)

[1 — 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)

2015

[5 — bwt2015a]

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

[4 — 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)

[3 — 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)

[2 — 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)

[1 — 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)

2014

[2 — dblv2014b]

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

[1 — bhk2014a]

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

2013

[4 — 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)

[3 — dbl2013a]

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

[2 — wtb2013a]

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

[1 — twb2013a]

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

2012

[3 — 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)

[2 — 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)

[1 — 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)

2011

[3 — 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)

[2 — 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)

[1 — 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)

2010

[4 — 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)

[3 — 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)

[2 — 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)

[1 — 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)

2009

[7 — 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)

[6 — 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)

[5 — 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)

[4 — 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)

[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 — 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)

2008

[2 — 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)

[1 — 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)

2007

[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 — 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)

[4 — 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)

[3 — 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)

[2 — 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)

[1 — 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)

2006

[1 — 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)

2004

[2 — 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)

[1 — 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)

Last updated: Thu, 12 Oct 2023 16:16