UP

Publications

Most publishers tend to introduce various small (but not necessarily irrelevant) errors into manuscripts, before they publish them (sic!). (For example, they love to replace \frac{a+b}{2} with a+b/2). The risk is high for journal publications, lower for book chapters, and generally low for conference publications. In any case, please drop an email to , if you find an error.

Journals

  1. Ollivier, Y., L. Arnold, A. Auger and N. Hansen (accepted for publication with revisions). Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles. Journal of Machine Learning Research (abstract and preprint).
  2. Auger, A., and N. Hansen (2016). Linear Convergence of Comparison-based Step-size Adaptive Randomized Search via Stability of Markov Chains, SIAM Journal on Optimization (accepted for publication, abstract and preprint).
  3. Chotard, A., A. Auger and N. Hansen (2015). Markov Chain Analysis of Cumulative Step-size Adaptation on a Linear Constraint Problem. Evolutionary Computation Journal, 23(4), pp. 611-640, MIT Press (abstract & pdf & bibtex).
  4. Éltetö, T., N. Hansen, C. Germain-Renaud and P. Bondon (2012). Scalable structural break detection. Applied Soft Computing, 12(11), pp. 3408-3420, Elsevier; (abstract & pdf & bibtex, DOI).
  5. Hansen, N., R. Ros, N. Mauny, M. Schoenauer and A. Auger (2011). Impacts of Invariance in Search: When CMA-ES and PSO Face Ill-Conditioned and Non-Separable Problems. Applied Soft Computing 11, 5755-5769, Elsevier; (abstract & pdf & bibtex, DOI, or mail me to get the final typeset version impacts-ASCO2011-author-personal.pdf).
  6. Jebalia, M., A. Auger and N. Hansen (2011). Log-Linear Convergence and Divergence of the Scale-Invariant (1+1)-ES in Noisy Environments, Algorithmica, 59(3), pp. 425-460 (paper draft in pdf, bibtex).
  7. Suttorp, T., N. Hansen and C. Igel (2009). Efficient Covariance Matrix Update for Variable Metric Evolution Strategies, Machine Learning, 75, pp. 167-197; (abstract & erratum, paper in pdf 1MB, bibtex).
  8. Hansen, N., A.S.P. Niederberger, L. Guzzella and P. Koumoutsakos (2009). A Method for Handling Uncertainty in Evolutionary Optimization with an Application to Feedback Control of Combustion. IEEE Transactions on Evolutionary Computation, 13(1), pp. 180-197; (abstract, pdf 2.7MB, bibtex, noise measurement source code as given in the appendix).
  9. Igel, C., N. Hansen and S. Roth (2007). Covariance Matrix Adaptation for Multi-objective Optimization. Evolutionary Computation, 15(1), pp.1-28; (abstract and pdf , bibtex).
  10. Glass, C.W., A.R. Oganov and N. Hansen (2006). USPEX - evolutionary crystal structure prediction. Computer Physics Communications, 175, pp. 713-720; (abstract, paper in pdf, bibtex).
  11. Hansen, N. (2006). An Analysis of Mutative σ-Self-Adaptation on Linear Fitness Functions. Evolutionary Computation, 14(3), pp. 255-275; (abstract, paper in pdf 1.4MB, bibtex).
  12. Kern, S., S.D. Müller, N. Hansen, D. Büche, J. Ocenasek and P. Koumoutsakos (2004). Learning Probability Distributions in Continuous Evolutionary Algorithms - A Comparative Review. Natural Computing, 3(1), pp. 77-112; (abstract & errata, corrected paper draft in pdf 830kB, bibtex).
  13. Hansen, N., S.D. Müller and P. Koumoutsakos (2003). Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). Evolutionary Computation, 11(1), pp. 1-18; (abstract & errata, paper in pdf, bibtex).
  14. Hansen, N. and A. Ostermeier (2001). Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation, 9(2), pp. 159-195; (abstract & errata, paper in pdf 1.1MB, paper in gzipped ps, bibtex).
  15. Ostermeier, A., A. Gawelczyk and N. Hansen (1994). A derandomized approach to self-adaptation of evolution strategies. Evolutionary Computation, 2(4), pp. 369-380;  (abstract, paper in pdf, paper in gzipped ps, bibtex).

Book Chapters, Editorials

  1. Hansen, N., D.V. Arnold and A. Auger (2015). Evolution Strategies. In Janusz Kacprzyk and Witold Pedrycz (Eds.): Handbook of Computational Intelligence, Springer, Chapter 44, pp.871-898 (pdf).
  2. Hansen, N. and A. Auger (2014). Principled design of continuous stochastic search: From theory to practice. In Y. Borenstein and A. Moraglio, eds.: Theory and Principled Methods for Designing Metaheustics. Springer, pp.145-180; (paper (pdf with erratum), at Springer, at HAL (with erratum)).
  3. Auger, A. and N. Hansen (2011). Theory of Evolution Strategies: A New Perspective. In A. Auger and B. Doerr, eds.: Theory of Randomized Search Heuristics: Foundations and Recent Developments. World Scientific Publishing, pp. 289-325 (paper and erratum in pdf, bibtex).
  4. Collange, G., N. Delattre, N. Hansen, I. Quinquis and M. Schoenauer (2010). Multidisciplinary Optimisation in the Design of Future Space Launchers. In P. Breitkopf and R. F. Coelho, eds.: Multidisciplinary Design Optimization in Computational Mechanics, Wiley, pp.487-496; (draft in pdf, bibtex).
  5. Collette, Y., N. Hansen, G. Pujol, D. Salazar Aponte and R. Le Riche (2010). On Object-Oriented Programming of Optimizers - Examples in Scilab. In P. Breitkopf and R. F. Coelho, eds.: Multidisciplinary Design Optimization in Computational Mechanics, Wiley, pp. 527-565; (draft in pdf 1.4MB, bibtex).
  6. Collette, Y., N. Hansen and G. Pujol (2009). Vers une Programmation Orientée Objet des Optimiseurs. Chapter 7 in Optimisation multidisciplinaire en mécanique 2. Réduction de modèles, robustesse, fiabilité, réalisations logicielles, series Méthodes Numériques en Mécanique, Hermes Science & Lavoisier, ISBN 978-2-7462-2196-3; (draft in pdf 790kB).
  7. Keijzer, M., G. Antoniol, C.B. Congdon, K. Deb, B. Doerr, N. Hansen, J.H. Holmes, G.S. Hornby, D. Howard, J. Kennedy, S. Kumar, F.G. Lobo, J.F. Miller, J. Moore, F. Neumann, M. Pelikan, J. Pollack, K. Sastry, K. Stanley, A. Stoica, E-G. Talbi, I. Wegener (eds, 2008). GECCO Genetic and Evolutionary Computation Conference, Proceedings, ACM, 2008.
  8. Hansen, N. (2006). The CMA Evolution Strategy: A Comparing Review. In J.A. Lozano, P. Larrañga, I. Inza and E. Bengoetxea (eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. pp. 75-102, Springer; (abstract and erratum, paper in pdf 1.5MB, bibtex).

Books, Monographs

  1. Hansen, N. (2010). Variable Metrics in Evolutionary Computation. Habilitation ŕ diriger des recherches, Université Paris-Sud (pdf).
  2. Hansen, N. (1998). Verallgemeinerte individuelle Schrittweitenregelung in der Evolutionsstrategie. Eine Untersuchung zur entstochastisierten, koordinatensystemunabhängigen Adaptation der Mutationsverteilung. PhD thesis, D83, Technical University Berlin, ISBN 3-933346-29-0. Berlin: Mensch und Buch Verlag; (abstract, pdf 890kB, bibtex).
  3. Schöneburg, E., N. Hansen and A. Gawelczyk (1992). Neuronale Netzwerke. Einführung, Überblick und Anwendungsmöglichkeiten. Haar: Markt&Technik Verlag. Dritte, erweiterte Auflage.

Peer-Reviewed Conference Proceedings

  1. Ait Elhara, O., A. Auger and N. Hansen (2016). Permuted Orthogonal Block-Diagonal Transformation Matrices for Large Scale Optimization Benchmarking. In Genetic and Evolutionary Computation Conference (GECCO 2016), Proceedings, ACM (abstract & paper).
  2. Akimoto, Y., and N. Hansen (2016). Projection-Based Restricted Covariance Matrix Adaptation for High Dimension. In Genetic and Evolutionary Computation Conference (GECCO 2016), Proceedings, ACM (abstract & paper).
  3. Brockhoff, D., T.D. Tran, N. Hansen (2015). Benchmarking Numerical Multiobjective Optimizers Revisited. In Genetic and Evolutionary Computation Conference (GECCO 2015), Proceedings, pp. 639-646, ACM (abstract & paper).
  4. Hansen, N., 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 (paper at Springer, paper and erratum at HAL).
  5. Loshchilov, I., M. Schoenauer, M. Sebag, N. Hansen (2014). Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES. In T. Bartz-Beielstein et al (eds.), Parallel Problem Solving from Nature - PPSN XIII, pp. 70-79, Springer (paper at Springer, paper at HAL, best paper award).
  6. Akimoto, Y., A. Auger, and N. Hansen (2014). Comparison-Based Natural Gradient Optimization in High Dimension. In Genetic and Evolutionary Computation Conference (GECCO 2014), Proceedings, ACM (pdf).
  7. Chotard, A., A. Auger, and N. Hansen (2014). Markov Chain Analysis of Evolution Strategies on a Linear Constraint Optimization Problem. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2014 (abstract, bibtex, paper on HAL, abstract, paper on arXiv).
  8. Ait Elhara, O., A. Auger and N. Hansen (2013). A Median Success Rule for Non-Elitist Evolution Strategies: Study of Feasibility. In Genetic and Evolutionary Computation Conference (GECCO 2013), Proceedings, ACM, pp. 415-422 (paper at ACM (restricted, without appendix), paper at HAL (open access, with appendix)).
  9. Akimoto, Y., A. Auger and N. Hansen (2012). Convergence of the continuous time trajectories of isotropic evolution strategies on monotonic C2-composite functions. In Parallel Problem Solving from Nature - PPSN XII, pp. 42-51, Springer (abstract and paper, paper on arxiv).
  10. Chotard, A., A. Auger and N. Hansen (2012). Cumulative Step-Size Adaptation on Linear Functions. In Parallel Problem Solving from Nature - PPSN XII, pp. 72-81, Springer (abstract and paper).
  11. Arnold, D.V., and N. Hansen (2012). A (1+1)-CMA-ES for constrained optimisation. In Genetic and Evolutionary Computation Conference (GECCO 2012), Proceedings, ACM, pp. 297-304 (paper at ACM (restricted), paper at HAL (open access)).
  12. Auger, A., D. Brockhoff and N. Hansen (2011). Mirrored Sampling in Evolution Strategies With Weighted Recombination. In Genetic and Evolutionary Computation Conference (GECCO 2011), Proceedings, ACM (pdf, doi&bibtex).
  13. Auger, A., D. Brockhof, and N. Hansen (2011). Analyzing the Impact of Mirrored Sampling and Sequential Selection in Elitist Evolution Strategies. In Foundations of Genetic Algorithms (FOGA), Proceedings, ACM (abstract, pdf, bibtex, pdf only).
  14. Brockhoff, D., A. Auger, N. Hansen, D. V. Arnold and T. Hohm (2010). Mirrored Sampling and Sequential Selection for Evolution Strategies. In R. Schaefer et al., editors, Parallel Problem Solving from Nature (PPSN XI), pp. 11-21, Springer (abstract, pdf, bibtex).
  15. Arnold, D.V. and N. Hansen (2010). Active covariance matrix adaptation for the (1+1)-CMA-ES. In Branke et al. (eds.), Genetic and Evolutionary Computation Conference GECCO 2010, Proceedings, pp. 385-392, ACM (abstract, pdf, bibtex (see export), best paper award).
  16. Voß, T., N. Hansen and C. Igel (2010). Improved step size adaptation for the MO-CMA-ES. In Branke et al. (eds.), Genetic and Evolutionary Computation Conference GECCO 2010, Proceedings, pp. 487-494, ACM (abstract, pdf, bibtex (see export)).
  17. Voß, T., N. Hansen and C. Igel (2009). Recombination for Learning Strategy Parameters in the MO-CMA-ES. In EMO Conference on Evolutionary Multi-Criterion Optimization 2009, Proceedings; (abstract, paper in pdf, bibtex).
  18. Hansen, N. (2008). Adaptive Encoding: How to Render Search Coordinate System Invariant. In Rudolph et al. (eds.) Parallel Problem Solving from Nature, PPSN X, Proceedings, pp. 205-214, Springer (abstract and paper, bibtex, encoding update source code).
  19. Ros, R. and N. Hansen (2008). A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. In Rudolph et al. (eds.) Parallel Problem Solving from Nature, PPSN X, Proceedings, pp. 296-305, Springer (abstract and paper).
  20. Kern S., Hansen N. and Koumoutsakos P. (2007). Optimization of Simulated Fish Swimming using Efficient Local Quadratic Meta-models and Evolution Strategies. Eurogen 2007, Jyväskylä, Finland, June 2007. (abstract, paper in pdf 1.5MB)
  21. Igel, C., T. Suttorp and N. Hansen (2007). Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES. In S. Obayashi et al. (eds.), Proceedings of the Fourth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2007), pp.171-185, Springer (paper in pdf).
  22. Hansen, N., F. Gemperle, A. Auger and P. Koumoutsakos (2006). When Do Heavy-Tail Distributions Help? In Ninth International Conference on Parallel Problem Solving from Nature PPSN IX, Proceedings, pp.62-71, Berlin: Springer; (abstract, paper draft pdf 5.7MB, bibtex).
  23. Kern, S., N. Hansen and P. Koumoutsakos (2006). Local Meta-Models for Optimization Using Evolution Strategies. In Ninth International Conference on Parallel Problem Solving from Nature PPSN IX, Proceedings, pp.939-948, Berlin: Springer; (abstract, paper draft in pdf).
  24. Auger, A. and N. Hansen (2006). Reconsidering the Progress Rate Theory for Evolution Strategies in Finite Dimensions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp.445-452, ACM Press (abstract & erratum & bibtex, paper in pdf).
  25. Igel, C., T. Suttorp and N. Hansen (2006). A Computational Efficient Covariance Matrix Update and a (1+1)-CMA for Evolution Strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp.453-460, ACM Press; (abstract & bibtex, paper in pdf).
  26. Auger, A. and Hansen, N. (2005). A Restart CMA Evolution Strategy With Increasing Population Size. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005, pp.1769-1776 (abstract & erratum, paper in pdf, bibtex).
  27. Auger, A. and Hansen, N. (2005). Performance Evaluation of an Advanced Local Search Evolutionary Algorithm. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005, pp.1777-1784 (abstract & erratum, paper in pdf).
  28. Hansen, N. and S. Kern (2004). Evaluating the CMA Evolution Strategy on Multimodal Test Functions. In Eighth International Conference on Parallel Problem Solving from Nature PPSN VIII, Proceedings, pp. 282-291, Berlin: Springer; (abstract and errata, paper in pdf, paper in ps, bibtex).
  29. Ocenasek, J., S. Kern, N. Hansen and P. Koumoutsakos (2004). A Mixed Bayesian Optimization Algorithm with Variance Adaptation. In Eighth International Conference on Parallel Problem Solving from Nature PPSN VIII, Proceedings, pp. 352-361, Berlin: Springer (paper in pdf).
  30. Müller, S.D., N. Hansen and P. Koumoutsakos (2002). Increasing the Serial and the Parallel Performance of the CMA-Evolution Strategy with Large Populations. In Seventh International Conference on Parallel Problem Solving from Nature PPSN VII, Proceedings, pp. 422-431, Berlin: Springer; (abstract, paper in pdf).
  31. Hansen , N. (2000). Invariance, Self-Adaptation and Correlated Mutations in Evolution Strategies. Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI), Proceedings, pp. 355-364; (abstract, paper in pdf, paper in gzipped ps, bibtex).
  32. Ostermeier, A. and N. Hansen (1999). An evolution strategy with coordinate system invariant adaptation of arbitrary normal mutation distributions within the concept of mutative strategy parameter control. In W.Banzhaf, J.Daida, A.Eiben, M.H.Garzon, V.Honavar, M.Jakiela, R.E.Smith (eds.), GECCO-99 Proceedings of the Genetic and  Evolutionary Computation Conference, pp. 902-909, San Francisco: Morgan Kaufmann Publishers;  (abstract, paper in pdf, paper in gzipped ps).
  33. Hansen, N. and A. Ostermeier (1997). Convergence properties of evolution strategies with the derandomized covariance matrix adaptation: The (μ/μI, λ)-ES. In EUFIT'97, 5th Europ.Congr.on Intelligent Techniques and Soft Computing, Proceedings, Aachen, pp. 650-654. Verlag Mainz, Wissenschaftsverlag; (abstract, paper in pdf, paper in gzipped ps).
  34. Hansen, N. and A. Ostermeier (1996). Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 312-317; (abstract, paper in pdf, paper in gzipped ps).
  35. Hansen, N., A. Gawelczyk and A. Ostermeier (1995). Sizing the population with respect to the local progress in (1, λ)-evolution strategies – a theoretical analysis. In 1995 IEEE International Conference on Evolutionary Computation Proceedings, pp. 80-85; (abstract, paper in pdf, paper in gzipped ps).
  36. Hansen, N., A. Ostermeier and A. Gawelczyk (1995). On the adaptation of arbitrary normal mutation distributions in evolution strategies: The generating set adaptation. In L. Eshelman (Ed.), Proceedings of the Sixth International Conference on Genetic Algorithms, Pittsburgh, pp. 57-64. Morgan Kaufmann; (abstract & errata, paper in pdf, paper in gzipped ps).
  37. Ostermeier, A., A. Gawelczyk and N. Hansen (1994). Step-size adaptation based on non-local use of selection information. In Y. Davidor, H.-P. Schwefel and R. Männer (eds.), Parallel Problem Solving from Nature--PPSN IV, Proceedings, Jerusalem, pp. 189-198. Springer; (paper in pdf 600kB).

More Proceedings

  1. Nikolaus Hansen (2014). CMA-ES: A Function Value Free Second Order Optimization Method. In PGMO-COPI'14 Conference on Optimization and Practices in Industry, abstract and pdf.
  2. Anne Auger, A., D. Brockhoff, and N. Hansen (2013). Benchmarking the local metamodel CMA-ES on the noiseless BBOB'2013 test bed. In Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, workshop on Black-Box Optimization Benchmarking (BBOB'2013), pp. 1225-1232, ACM (pdf).
  3. Akimoto, Y., A. Auger, and N. Hansen (2012). Linear convergence proof for adaptive-ES algorithm via continuous-time approximation. In Proceedings of the 6th Evolutionary Computation Symposium, Nagano, Japan.
  4. Brockhoff, D., A. Auger, and N. Hansen (2012). Comparing mirrored mutations and active covariance matrix adaptation in the IPOP-CMA-ES on the noiseless BBOB testbed. In Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion '12, pp. 297-304, ACM (pdf).
  5. Brockhoff, D., A. Auger, and N. Hansen (2012). 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 Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion '12, pp. 291-296, ACM (pdf).
  6. Brockhoff, D., A. Auger, and N. Hansen (2012). On the impact of a small initial population size in the IPOP active CMA-ES with mirrored mutations on the noiseless BBOB testbed. In Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion '12, pp. 285-290, ACM (pdf).
  7. Brockhoff, D., A. Auger, and N. Hansen (2012). On the effect of mirroring in the IPOP active CMA-ES on the noiseless BBOB testbed. In Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion, GECCO Companion '12, pp. 277-284, ACM (pdf).
  8. Collange, G., S. Reynaud and N. Hansen (2010). Covariance Matrix Adaptation Evolution Strategy for Multidisciplinary Optimization of Expendable Launcher Families. In 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, Proceedings; (paper in pdf).
  9. Auger, A., D. Brockhoff, N. Hansen (2010). Benchmarking the (1,4)-CMA-ES with Mirrored Sampling and Sequential Selection on the Noiseless BBOB-2010 Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference 2010, ACM, pp. 1617-1623 (abstract and pdf).
  10. Auger, A., D. Brockhoff, N. Hansen (2010). Benchmarking the (1,4)-CMA-ES with Mirrored Sampling and Sequential Selection on the Noisy BBOB-2010 Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference 2010, ACM, pp. 1625-1631 (abstract and pdf).
  11. Hansen, N., A. Auger, R. Ros, S. Finck, P. Posik (2010). Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference 2010, ACM, pp. 1689-1696 (paper in pdf, erratum).
  12. Hansen, N., R. Ros (2010). Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noiseless Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference 2010, ACM, pp. 1673-1680 (paper in pdf).
  13. Hansen, N., R. Ros (2010). Benchmarking a Weighted Negative Covariance Matrix Update on the BBOB-2010 Noisy Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference 2010, ACM, pp. 1681-1687 (paper in pdf).
  14. Auger, A and N. Hansen (2009). Benchmarking the (1+1)-CMA-ES on the BBOB-2009 Function Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM, pp. 2459-2465.
  15. Auger, A and N. Hansen (2009). Benchmarking the (1+1)-CMA-ES on the BBOB-2009 Noisy Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM. pp. 2467-2471.
  16. Auger, A., N. Hansen, J.M. Perez Zerpa, R. Ros and M. Schoenauer (2009). Experimental comparisons of derivative free optimization algorithms (invited talk). In J. Vahrenhold (Ed.), Experimental algorithms: 8th international symposium SEA 2009, Dortmund, LNCS 5526, pp. 3-15, Springer (abstract and pdf, google books, bibtex).
  17. Auger, A., N. Hansen, J.M. Perez Zerpa, R. Ros and M. Schoenauer (2009). Empirical comparisons of several derivative free optimization algorithms. In Acte du 9ime colloque national en calcul des structures, Giens; (paper in pdf).
  18. Hansen, N. (2009). Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM, pp. 2389-2395 (abstract and pdf, erratum, source code, bibtex).
  19. Hansen, N. (2009). Benchmarking a BI-Population CMA-ES on the BBOB-2009 Noisy Testbed. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM, pp. 2397-2402 (abstract and pdf, erratum, bibtex).
  20. Hansen, N. (2009). Benchmarking the Nelder-Mead Downhill Simplex Algorithm With Many Local Restarts. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, ACM, pp. 2403-2408 (abstract and pdf, bibtex).
  21. Hansen, N., A.S.P. Niederberger, L. Guzzella and P. Koumoutsakos (2008). Evolutionary Optimization of Feedback Controllers for Thermoacoustic Instabilities. J.F.Morrison, D.M.Birch, and P.Lavoie (eds.) IUTAM Symposium on Flow Control and MEMS, Proceedings of the IUTAM Symposium held at the Royal Geographical Society, 19-22 September 06, Springer, 2008 (abstract, paper draft in pdf).
  22. Müller, S.D., N.N. Schraudolph, P. Koumoutsakos and N.Hansen (2002). Step Size Adaptation in Evolution Strategies - Two Approaches. In A. Barry (Ed.), Workshop on Learning and Adaptation in Evolutionary Computation, Workshop Proceedings of the 2002 Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 161-164, San Francisco: Morgan Kaufmann Publishers.
  23. Rechenberg, I., F. Brand, N. Hansen, M. Herdy and A. Ostermeier (1995). Theorie der Evolutionsstrategie – Von der Zufallssuche zur intelligenten Strategie. In G. Wolf, R. Schmidt and M. van der Meer (eds.), Tagungsband zum Statusseminar des BMBF Bioinformatik 1995, 12484 Berlin, pp. 293-303. Projektträger Informationstechnik des BMBF bei der DLR, Abteilung Informationsverarbeitung.

Reports and Other Contributions

  1. Hansen, N. (2016). The CMA Evolution Strategy: A Tutorial. ArXiv e-prints, arXiv:1604.00772.
  2. Auger, A. and N. Hansen (2013). Linear convergence on positively homogeneous functions of a comparison based step-size adaptive randomized search: the (1+1) ES with generalized one-fifth success rule. CoRR, abs/1310.8397 (abstract, pdf, bibtex on arXiv).
  3. Auger, A. and N. Hansen (2013). On proving linear convergence of comparison-based step-size adaptive randomized search on scaling-invariant functions via stability of markov chains. CoRR, abs/1310.7697 (abstract, pdf, bibtex on arXiv).
  4. Ollivier, Y., L. Arnold, A. Auger, N. Hansen (2013). Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles. CoRR, abs/1106.3708v2 (abstract, pdf, bibtex on arXiv).
  5. Hansen, N. (2011). A CMA-ES for Mixed-Integer Nonlinear Optimization. INRIA Research Report RR-7751 (abstract and pdf).
  6. Hansen, N. (2011). Injecting External Solutions Into CMA-ES. INRIA Research Report RR-7748. CoRR, abs/1110.4181 (abstract, pdf, bibtex on arXiv).
  7. Arnold, L., A. Auger, N. Hansen, Y. Ollivier (2011). Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles, technical report on HAL: hal-00601503, on arXiv: arXiv:1106.3708v1.
  8. Hansen, N., A. Auger, S. Finck R. and Ros (2010), Real-Parameter Black-Box Optimization Benchmarking 2010: Experimental Setup, INRIA Research Report RR-7215 (abstract and report, bibtex).
  9. Auger A., S. Finck, N. Hansen, R. Ros (2010). BBOB 2009: Comparison Tables of All Algorithms on All Noiseless Functions, INRIA Research Report RT-0383; (abstract/PDF/bibtex).
  10. Auger A., S. Finck, N. Hansen, R. Ros (2010). BBOB 2009: Comparison Tables of All Algorithms on All Noisy Functions, INRIA Research Report RT-0383; (abstract/PDF/bibtex).
  11. Finck, S., N. Hansen, R. Ros and A. Auger (2009), Real-Parameter Black-Box Optimization Benchmarking 2009: Presentation of the Noisy Functions, Research Center PPE, Report Number 2009/21 (pdf 20MB, bibtex).
  12. Finck, S., N. Hansen, R. Ros and A. Auger (2009), Real-Parameter Black-Box Optimization Benchmarking 2009: Presentation of the Noiseless Functions, Research Center PPE, Report Number 2009/20 (pdf 13MB, bibtex).
  13. Hansen, N., S. Finck, R. Ros and A. Auger (2009), Real-Parameter Black-Box Optimization Benchmarking 2009: Noisy Functions Definitions, INRIA Research Report RR-6869 (pdf, bibtex).
  14. Hansen, N., S. Finck, R. Ros and A. Auger (2009), Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions, INRIA Research Report RR-6829 (abstract and report, bibtex).
  15. Hansen, N., A. Auger, S. Finck R. and Ros (2009), Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup, INRIA Research Report RR-6828 (abstract and report, bibtex).
  16. Hansen, N. (2008). CMA-ES with Two-Point Step-Size Adaptation. INRIA Research Report RR-6527 (abstract and report).
  17. Hansen, N. (2008). Adaptive Encoding for Optimization. INRIA Research Report RR-6518 (abstract and report).
  18. Ros, R. and N. Hansen (2008). A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. INRIA Research Report RR-6498 (abstract and report).
  19. Hansen, N, R. Ros, N. Mauny, M. Schoenauer and A. Auger (2008). PSO Facing Non-Separable and Ill-Conditioned Problems. INRIA Research Report RR-6447 (abstract and report).
  20. Suganthan, P.N., N. Hansen, J.J. Liang, K. Deb, Y. P. Chen, A. Auger, and S. Tiwari (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore and KanGAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur), May 2005 (report in pdf) .
  21. Igel, C., N. Hansen and S. Roth (2005). The Multi-objective Variable Metric Evolution Strategy, Part I. Technical Report, IRINI-2001-04, Institut für Neuroinformatik (report in pdf).
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  23. Ostermeier, A., A. Gawelczyk and N. Hansen (1993). A derandomized approach to self adaptation of evolution strategies. Technischer Report TR-03-93, Institut für Bionik und Evolutionstechnik der Technischen Universität Berlin; (abstract, paper in gzipped ps).
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