A. Auger and N. Hansen, Linear Convergence of Comparison-based Step-size Adaptive Randomized Search via Stability of Markov Chains, SIAM Journal on Optimization 26(3):1589-1624, 2016. (preprint)

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

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

A. Atamna, A. Auger, and N. Hansen. Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling. In Genetic and Evolutionary Computation Conference (GECCO 2017), ACM.

A. Atamna, A. Auger, and N. Hansen. Augmented Lagrangian Constraint Handling for CMA-ES—Case of a Single Linear Constraint. In Parallel Problem Solving from Nature (PPSN 2016), pages 181-191. Springer.

A. Atamna, A. Auger, and N. Hansen, Analysis of Linear Convergence of a (1+1)-ES with Augmented Lagrangian Constraint Handling, Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States.

Y. Akimoto and N. Hansen, Projection-Based Restricted Covariance Matrix Adaptation for High Dimension Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States (pdf).

O. Ait Elhara, A. Auger, and N. Hansen, Permuted Orthogonal Block-Diagonal Transformation Matrices for Large Scale Optimization Benchmarking. Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States (pdf).

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 | suppl. material)

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)

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

N. Hansen, A. Atamna, and A. Auger (2014). How to Assess Step-Size Adaptation Mechanisms in Randomized Search. In T. Bartz-Beielstein et al (eds.), Parallel Problem Solving from Nature - PPSN XIII, pp. 60-69, Springer (pdf)

Y. Akimoto, A. Auger, and N. Hansen (2014). Comparison-Based Natural Gradient Optimization in High Dimension. Proceedings of the 2014 conference on Genetic and evolutionary computation (GECCO 2014), pp. 373-380, ACM (pdf)

A. Chotard, A. Auger, and N. Hansen (2014). Markov Chain Analysis of Evolution Strategies on a Linear Constraint Optimization Problem. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2014), pp. 159-166 (pdf)

B. Bischl, S. Wessing, N. Bauer, K. Friedrichs, and C. Weihs. MOI-MBO: Multiobjective infill for parallel model-based optimization (ACCEPTED, PRELIM. VERSION AVAILABLE). In Learning and Intelligent Optimization Conference (LION), 2014. (bib | pdf)

O. Ait Elhara, A. Auger and N. Hansen. A Median Success Rule for Non-Elitist Evolution Strategies: Study of Feasibility, in Christian Blum and Enrique Alba, eds., Genetic and Evolutionary Computation Conference, Jul 2013, Amsterdam, Netherlands. ACM Press, pp. 415-422. pdf.

T. Wagner and 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. LNCS 7811, Springer Verlag, 2013 (pdf | doi).

B. Derbel and 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. LNCS 7811, Springer Verlag, 2013 (pdf | doi).

H. Mohammadi, R. Le Riche and E. Touboul. Making EGO and CMA-ES complementary for global optimization. Lecture Notes in Computer Science, vol.8994, Learning and Intelligent Optimization (revised and selected papers of the LION9 conference), Springer, May 2015, pp.287-292. (doi)

H. Trautmann, 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. (doi)

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). ACM, 2016.

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). ACM, 2016.

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). ACM, 2016.

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). ACM, 2016.

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). ACM, 2016.

T. Tušar and B. Filipič. Performance of the DEMO algorithm on the bi-objective BBOB test suite. In GECCO (Companion) workshop on Black-Box Optimization Benchmarking (BBOB'2015). ACM, 2016.

A. Atamna. Benchmarking IPOP-CMA-ES-TPA and IPOP-CMA-ES-MSR on the BBOB Noiseless Testbed. In GECCO (Companion) workshop on Black-Box Ootimization Benchmarking (BBOB'2016), pages 1135-1142. ACM, 2015.

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)

H. Mohammadi, R. Le Riche, E. Touboul, X. Bay, and N. Durrande An analytic comparison of regularizations methods for Gaussian processes, GdR Mascot-Num annual conference, April 2015, Saint-Etienne, France.

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

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

H. Mohammadi, R. Le Riche, E. Touboul and X. Bay, On regularization techniques in statistical learning by Gaussian processes, International France-China Workshop, NICST’2013 (New and smart Information Communication Science and Technology to support Sustainable Development), September 2013, Clermont Ferrand, France.

S. Zapotecas-Martinez, B. Derbel, A. Liefooghe, D. Brockhoff, H. Aguirre, and K. Tanaka. Injecting CMA-ES into MOEA/D. Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 783-790, ACM pdf

B. Derbel, D. Brockhoff, A. Liefooghe, and S. Verel (2014). On the Impact of Multiobjective Scalarizing Functions. In T. Bartz-Beielstein et al (eds.), Parallel Problem Solving from Nature - PPSN XIII, pp. 548–558, Springer (pdf)

G. Rudolph, C. Grimme, C., O. Schütze, and H. Trautmann (2014). An Aspiration Set EMOA Based on Averaged Hausdorff Distances. In Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8), Gainesville, Florida, USA. pdf

H. Trautmann, T. Wagner, D. Biermann and C. Weihs (2013). Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index. Journal of Multi-Criteria Decision Analysis 20 (5-6), pp. 319-337.

G. Rudolph, H. Trautmann, S. Sengupta and O. Schütze (2013). Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation. In Purshouse, R., Fleming, P., Fonseca, C., Greco, S. and Shaw, J. (Eds.), Evolutionary Multi-Criterion Optimization — 7th International Conference, EMO 2013, Sheffield, UK, Proceedings (pp. 443–458). Lecture Notes in Computer Science: Vol. 7811. Springer.

I. Loshchilov, M. Schoenauer, and M/ Sebag. BI-population CMA-ES Algorithms with Surrogate Models and Line Searches. In Workshop Proceedings of the (ACM-GECCO) Genetic and Evolutionary Computation Conference, Jul 2013, Amsterdam, Netherlands. pp. 8. pdf

G. Marceau, P. Savéant, M. Schoenauer. Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management. In Carlos A. Coello Coello and Yaochu Jin, eds., IEEE Congress on Evolutionary Computation, Jun 2013, Cancun, Mexico. IEEE Press, pp. 1548-1555. pdf

M. Redouane Khouadjia, M. Schoenauer, V. Vidal, J. Dréo, and P/ Savéant. Pareto-Based Multiobjective AI Planning, in Francesca Rossi, ed., Proc. 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, Beijing, China. pdf

I. Loshchilov, M. Schoenauer, and M. Sebag. Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CMA-ES (saACM-ES), in Christian Blum and Enrique Alba, eds., Genetic and Evolutionary Computation Conference (GECCO 2013), Jul 2013, Amsterdam, Netherlands. pp. 439-446. pdf

M. Khouadjia, M. Schoenauer, V. Vidal, J. Dréo, and P. Savéant. Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark. In Conference on Evolutionary Multi-Criterion Optimization (EMO 2013), pp 36–50. LNCS 7811, Springer, 2013.

H. Mohammadi, R. Le Riche, E. Touboul. Small ensemble of kriging models for optimization. HAL Technical report no. hal-01285225, Mar. 2016. hal link

H. Mohammadi, R. Le Riche and E. Touboul, A detailed analysis of kernel parameters in Gaussian process-based optimization, HAL Technical report no. hal-01246677 version 2, Feb. 2016, continuation on D1.2.hal link

H. Mohammadi, R. Le Riche, N. Durrande, E. Touboul and X. Bay, An analytic comparison of regularization methods for Gaussian Processes, HAL Technical report no. hal-01264192, Jan. 2016, as D1.3 and M1.4 hal link.

H. Mohammadi, R. Le Riche and E. Touboul, A comparison of EGO and CMA-ES global optimization algorithms, Aug. 2014, as M1.1 and D1.2.

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

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 (arXiv)

T. Tušar, D. Brockhoff, N. Hansen, and A. Auger: COCO: The Bi-objective Black Box Optimization Benchmarking (bbob-biobj) Test Suite ArXiv e-prints, arXiv:1604.00359. 2016 (pdf)

D. Brockhoff, T. Tušar, D. Tušar, T. Wagner, N. Hansen, and A. Auger, Biobjective Performance Assessment with the COCO Platform ArXiv e-prints, arXiv:1605.01746. 2016 (arXiv)

N. Hansen, A. Auger, D. Brockhoff, D. Tušar, and T. Tušar, COCO: Performance Assessment ArXiv e-prints, arXiv:1605.03560. 2016 (arXiv)

A. Atamna. Analysis of Randomized Adaptive Algorithms for Black-Box Continuous Constrained Optimization, defended in January 2017 at the Universy of Paris-Saclay. (pdf)

H. Mohammadi. Kriging-based black-box global optimization: analysis and new algorithms, PhD thesis, defended on April the 11th 2016 at the Ecole des Mines de St-Etienne.