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Crossing the Chasm

Participants

Alejandro Arbelaez , Luis Da Costa , Alvaro Fialho , Youssef Hamadi , Nikolaus Hansen , Balázs Kégl , Róbert Busa-Fekete, Philippe Rolet, Marc Schoenauer , Michèle Sebag

Research Themes


Many forefront techniques in both Machine Learning and Stochastic Search have been very successful in solving difficult real-world problems. However, their application to newly encountered problems, or even to new instances of known problems, remains a challenge, even for experienced researchers of the field - not to mention newcomers, even if they are skilled scientists or engineers from other areas. Theory and/or practical tools are still missing to make them crossing the chasm (from Geoffrey A. Moore's book about the diffusion of innovation).
The difficulties faced by the users arise mainly from the significant range of algorithm and/or parameter choices involved when using this type of approaches, and the lack of guidance as to how to proceed for selecting them. Moreover, state-of-the-art approaches for real-world problems tend to represent bespoke problem-specific methods which are expensive to develop and maintain.

More specifically, the following research conducted in TAO is concerned with Crossing the Chasm
  • Adaptive Operator Selection, or how to adapt the mechanism that chooses among the different variation operators in Evolutionary Algorithms. We have proposed two original features
    • Using a Multi-Armed Bandit algorithm for operator selection GECCO'08
    • Using Extreme values rather than averages as a reward for operators PPSN'08
    • On-going work is investigating the recombination of the above ideas with the Compass approach of our colleagues from Angers University (J.Maturana, F.Saubion: A Compass to Guide Genetic Algorithms. PPSN 2008: 256-265)
  • Adaptation for Continuous Optimization: building on the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, that adapts the covariance matrix of the Gaussian mutation of an Evolution Strategy based on the path followed by the evolution, several improvements and generalizations have been proposed:
    • an adaptive mechanism that can apply to any distribution-based search strategy has been proposed at PPSN'08 . The mechanism renders the underlying search strategy rotational invariant and facilitates an adaptation to non-separable problems. On non-separable, badly scaled problems the performance of many search algorithms can improve by orders of magnitude.
    • a version of CMA with linearly scaling computational complexity and linear space requirement has been proposed at PPSN'08 (compared to quadratic for the original algorithm). In high dimensional search spaces (larger than hundred) the new variant can be advantageous not only on cheap to evaluate search problems but even on very expensive non-separable problems.
  • Meta-parameter tuning for Machine Learning Algorithms: Non-parametric learning algorithms usually require the tuning of hyper-parameters that determine the complexity of the learning machine. Tuning this parameters is usually done manually based on (cross) validation schemes. The goal of this theme is to develop principled methods to carry out this optimization task automatically using global optimization algorithms. The theme is part of the MetaModel project.
  • Learning Heuristics Choice in Constraint Programming: several heuristics have been proposed to choose which branch to explore next within Constraint Programming algorithms. The idea we are exploring is to learn which one is the best given the characteristics of the current node of the tree (e.g. domain sizes, number of still unsatisfied constraints, etc).
  • Active Learning, or how to choose next sample depending on previously seems examples
  • Designing Problem Descriptors is a longer-term goal: being able to accurately describe a given problem (or instance) will allow us to then learn from extensive experiments what are the good algorithms/parameters for classes of instances, or even indvidual instances (see e.g. F.Hutter, Y.Hamadi, H.H.Hoos, and K.Leyton-Brown. Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms, CP'2006).


  • The goal of the Microsoft-INRIA Joint lab project on Adaptive search for E-Science , is precisely to automate the parameter tuning of search algorithms.
  • EvoTest is a European STRAP that started in September 2006, dealing with Evolutionary generation of test data. The work of TAO is to provide the Evolution Engine to the framework, including on the one hand the GUIDE interface for easy EA design, and adding to it automatic parameter tuning facilities.
  • MetaModel (Advanced methodologies for modeling interdependent systems - applications in experimental physics) ANR "jeune chercheur" project.

Publications about Crossing the Chasm


Journal Articles


2011


* Bouzarkouna, Z. , Ding, D. and Auger, A.. Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models. In: Computational Geosciences, Springer Netherlands : p. 1-18. 2011. ISSN: 1420-0597.10.1007/s10596-011-9254-2.  Click here to see the bib entry for this publication


* Hansen, N. , Ros, R. , Mauny, N. , Schoenauer, M. and Auger, A.. Impacts of Invariance in Search: When CMA-ES and PSO Face Ill-Conditioned and Non-Separable Problems. In: Applied Soft Computing, Elsevier, Vol. 11(8) : p. 5755–5769. 2011.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2010


* Fialho, A. , Da Costa, L. , Schoenauer, M. and Sebag, M.. Analyzing Bandit-based Adaptive Operator Selection Mechanisms. In: Annals of Mathematics and Artificial Intelligence – Special Issue on Learning and Intelligent Optimization, Springer Netherlands, Vol. 60 : p. 25-64. September 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2009


* Hansen, N. , Niederberger, A. , Guzzella, L. and Koumoutsakos, P.. A Method for Handling Uncertainty in Evolutionary Optimization with an Application to Feedback Control of Combustion. In: IEEE Transactions on Evolutionary Computation, IEEE, Vol. 13(1) : p. 180–197. 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Suttorp, T. , Hansen, N. and Igel, C.. Efficient Covariance Matrix Update for Variable Metric Evolution Strategies. In: Machine Learning, Springer, Vol. 72(2) : p. 167–197. 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2007


* Igel, C. , Hansen, N. and Roth, S.. Covariance Matrix Adaptation for Multi-objective Optimization. In: Evolutionary Computation, Vol. 15(1) : p. 1-28. 2007.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication



Book Chapters


2011


* Arbelaez, A. , Hamadi, Y. and Sebag, M.. Continuous Search in Constraint Programming. In Youssef Hamadi, Eric Monfroy and Frédéric Saubion, eds.: "Autonomous Search", Springer-Verlag. 2011.   Click here to see the bib entry for this publication


* Maturana, J. , Fialho, A. , Saubion, F. , Schoenauer, M. , Lardeux, F. and Sebag, M.. Adaptive Operator Selection and Management in Evolutionary Algorithms. In Hamadi and Y. et al, eds.: "Autonomous Search", Springer Verlag : p. 161-189. 2011.   Click here to see the bib entry for this publication



Papers in Conference Proceedings


2013


* Khouadjia, M. , Schoenauer, M. , Vidal, V. , Dréo, J. and Savéant, P.. Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark. In Robin C. Purshouse, Peter J. Fleming and Carlos M. Fonseca, eds.: "7th International Conference on Evolutionary Multi-Criterion Optimization", Springer Verlag, LNCS 7811 : p. 36-50. Sheffield, Royaume-Uni. Mar 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Khouadjia, M. , Schoenauer, M. , Vidal, V. , Dréo, J. and Savéant, P.. Pareto-Based Multiobjective AI Planning. In Sebastien Thrun and Francesca Rossi, eds.: "IJCAI'13 – Intl Joint Conference on Artificial Intelligence", AAAI : p. To appear. Beijing, China. Mar 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Khouadjia, M. , Schoenauer, M. , Vidal, V. , Dréo, J. and Savéant, P.. Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning. In Panos Pardalos and Guiseppe Nicosia, eds.: "LION7 – 7th Learning and Intelligent OptimizatioN Conference", Springer Verlag, LNCS : p. To appear. Catania, Italy. Mar 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Khouadjia, M. , Schoenauer, M. , Vidal, V. , Dréo, J. and Savéant, P.. Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning. In Martin Middendorf and Christian Blum, eds.: "EvoCOP – 13th European Conference on Evolutionary Computation in Combinatorial Optimisation", Springer Verlag, LNCS 7832 : p. 202-213. Vienna, Austria. Mar 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CMA-ES (saACM-ES). In C. Blum et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. to appear. July 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. BI-population CMA-ES Algorithms with Surrogate Models and Line Searches. In C. Blum et al., eds.: "Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference", ACM Press : p. to appear. July 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization. In: "Conference Francophone sur l'Apprentissage Automatique" : p. to appear. 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Shigeru Takano, Ilya Loshchilov, David Meunier, M. and Suzuki, E.. KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization. In: "Fourth International Joint Conference on Ambient Intelligence Dublin" : p. to appear. 2013.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2012


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy. In T. Soule et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 321–328. July 2012.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. Black-box Optimization Benchmarking of IPOP-saACM-ES on the BBOB-2012 Noisy Testbed. In T. Soule et al., eds.: "Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference", ACM Press : p. 261–268. July 2012.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. Black-box Optimization Benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 Noiseless Testbed. In T. Soule et al., eds.: "Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference", ACM Press : p. 175–182. July 2012.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. Black-box Optimization Benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 Noiseless Testbed. In T. Soule et al., eds.: "Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference", ACM Press : p. 269–276. July 2012.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2011


* Arbelaez, A. and Hamadi, Y.. Improving Parallel Local Search for SAT. In Carlos A. Coello Coello, eds.: "Learning and Intelligent Optimization, Fifth International Conference, LION 2011", LNCS 6683, Springer Verlag : p. 46-60. 2011.   Click here to see the bib entry for this publication


* Coutoux, A. , Hoock, J. , Sokolovska, N. , Teytaud, O. and Bonnard, N.. Continuous Upper Confidence Trees. In Carlos A. Coello Coello, eds.: "LION'11: Proc. 5th Conference on Learning and Intelligent OptimizatioN", LNCS 6683, Springer Verlag : p. 433-445. January 2011.   Click here to see the bib entry for this publication


* Li, K. , Fialho, A. and Kwong, S.. Multi-Objective Differential Evolution with Adaptive Control of Parameters and Operators. In C. A. Coello Coello, eds.: "LION'11: Proceedings of the 5th International Conference on Learning and Intelligent OptimizatioN", LNCS 6683, Springer Verlag : p. 473-4887. January 2011.   Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. Not all parents are equal for MO-CMA-ES. In: "Evolutionary Multi-Criterion Optimization 2011 (EMO 2011)", Springer Verlag, LNCS 6576 : p. 31-45. April 2011.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2010


* Arbelaez, A. , Hamadi, Y. and Sebag, M.. Building Portfolios for the Protein Structure Prediction Problem. In: "Workshop on Constraint Based Methods for Bioinformatics" : p. 42-46. July 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Arbelaez, A. , Hamadi, Y. and Sebag, M.. Continuous Search In Constraint Programming. In IEEE Press, eds.: "Proc. 22nd ICTAI" : p. 53-60. 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Arnold, D.V. and Hansen, N.. Active covariance matrix adaptation for the (1+1)-CMA-ES. In J. Branke et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 385-392. 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Bibai, J. , Savéant, P. , Schoenauer, M. and Vidal, V.. On the Generality of Parameter Tuning in Evolutionary Planning. In J. Branke et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 241-248. July 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Fialho, A. , Gong, W. and Cai, Z.. Probability Matching-based Adaptive Strategy Selection vs. Uniform Strategy Selection within Differential Evolution. In: "Workshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)" : p. 1527-1534. 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Fialho, A. , Ros, R. , Schoenauer, M. and Sebag, M.. Comparison-based Adaptive Strategy Selection with Bandits in Differential Evolution. In R. Schaefer et al., eds.: "Parallel Problem Solving from Nature (PPSN XI)", Springer, LNCS, Vol. 6238 : p. 194-203. 2010.   Click here to see the PDF file or the official link to it  Click here to see the Poster for this publication  Click here to see the bib entry for this publication


* Fialho, A. , Schoenauer, M. and Sebag, M.. Toward Comparison-based Adaptive Operator Selection. In J. Branke et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 767-774. July 2010.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Fialho, A. , Schoenauer, M. and Sebag, M.. Fitness-AUC Bandit Adaptive Strategy Selection vs. the Probability Matching one within Differential Evolution. In: "Workshop Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)" : p. 1535-1542. 2010.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Gong, W. , Fialho, A. and Cai, Z.. Adaptive Strategy Selection in Differential Evolution. In J. Branke et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 409-416. July 2010.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. Dominance-Based Pareto-Surrogate for Multi-Objective Optimization. In R. Takahashi et al., eds.: "Simulated Evolution and Learning (SEAL 2010)", LNCS 6457, Springer Verlag : p. 230-239. December 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. A Mono Surrogate for Multiobjective Optimization. In J. Branke et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 471-478. July 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Loshchilov, I. , Schoenauer, M. and Sebag, M.. A Pareto-Compliant Surrogate Approach for Multiobjective Optimization. In J. Branke et al., eds.: "Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference", ACM Press : p. 1979-1982. July 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Voß, T. , Igel, C. and Hansen, N.. Improved Step Size Adaptation for the MO-CMA-ES. In J. Branke et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 487-494. July 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2009


* Arbelaez, A. and Hamadi, Y.. Exploiting Weak Dependencies in Tree-based Search. In: "Proceedings of the 24th Annual ACM Symposium on Applied Computing", ACM Press : p. 1385-1391. ACM. 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Arbelaez, A. , Hamadi, Y. and Sebag, M.. Online Heuristic Selection in Constraint Programing. In: "International Symposium on Combinatorial Search". 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Da Costa, L. and Schoenauer, M.. Bringing Evolutionary Computation to Industrial Applications with GUIDE. In G. Raidl et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 1467-1474. ACM. 2009.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Fialho, A. , Da Costa, L. , Schoenauer, M. and Sebag, M.. Dynamic Multi-Armed Bandits and Extreme Value-based Rewards for Adaptive Operator Selection in Evolutionary Algorithms. In T. Stuetzle et al., eds.: "LION'09: Proceedings of the 3rd International Conference on Learning and Intelligent OptimizatioN", Springer Verlag, Vol. 5851 : p. 176-190. January 2009. Best Paper Award.  Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Fialho, A. , Schoenauer, M. and Sebag, M.. Analysis of Adaptive Operator Selection Techniques on the Royal Road and Long K-Path Problems. In G. Raidl et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 779-786. July 2009.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Maturana, J. , Fialho, A. , Saubion, F. , Schoenauer, M. and Sebag, M.. Extreme Compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection. In: "CEC'09: Proceedings of the IEEE International Conference on Evolutionary Computation", IEEE : p. 365-372. May 2009.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Voß, T. , Hansen, N. and Igel, C.. Recombination for Learning Strategy Parameters in the MO-CMA-ES. In: "Fifth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009)", Springer-Verlag : p. 155–168. 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2008


* Da Costa, L. , Fialho, A. , Schoenauer, M. and Sebag, M.. Adaptive Operator Selection with Dynamic Multi-Armed Bandits. In C. Ryan et al., eds.: "Genetic and Evolutionary Computation Conference (GECCO)", ACM Press : p. 913-920. ACM. July 2008.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


* Fialho, A. , Da Costa, L. , Schoenauer, M. and Sebag, M.. Extreme Value Based Adaptive Operator Selection. In Parallel Problem Solving from Nature (PPSN X) ed, eds.: "Parallel Problem Solving from Nature (PPSN X)", Springer, LNCS, Vol. 5199 : p. 175-184. September 2008.   Click here to see the PDF file or the official link to it  Click here to see the Poster for this publication  Click here to see the bib entry for this publication


* Hansen, N.. Adaptive Encoding: How to Render Search Coordinate System Invariant. In Parallel Problem Solving from Nature (PPSN X) ed, eds.: "Parallel Problem Solving from Nature (PPSN X)", LNCS, Vol. 5199 : p. 205–214. 2008.   Click here to see the PDF file or the official link to it  Click here to see the Poster for this publication  Click here to see the bib entry for this publication


* Ros, R. and Hansen, N.. A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. In Parallel Problem Solving from Nature (PPSN X) ed, eds.: "Parallel Problem Solving from Nature (PPSN X)", LNCS, Vol. 5199 : p. 296-305. 2008.   Click here to see the PDF file or the official link to it  Click here to see the Poster for this publication  Click here to see the bib entry for this publication


2007


* Gaudel, R. and Cornuéjols, A.. Combining feature ranking methods for high dimensional data analysis. In: "5th Workshop on Statistical Methods for Post-Genomic Data". Paris France. 2007.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Igel, C. , Suttorp, T. and Hansen, N.. Steady-state Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES. In S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu and T. Murata, eds.: "Fourth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2007)", Springer-Verlag, LNCS, Vol. 4403 : p. 171-185. 2007.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication



Habilitation and Ph.D. Theses


2010


* Fialho, A.. Adaptive Operator Selection for Optimization. PhD thesis, Université Paris-Sud - Paris XI. December 2010.   Click here to see the PDF file or the official link to it  Click here to see the Slides for this publication  Click here to see the bib entry for this publication


2009


* Ros, R.. Real-Parameter Black-Box Optimisation: Benchmarking and Designing Algorithms. PhD thesis, Université Paris-Sud - Paris XI. Orsay, France. December 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2008


* Baskiotis, N.. Évaluation d'Algorithmes pour et par l'Apprentissage. PhD thesis, Université Paris-Sud - Paris XI. 2008.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication



Technical Reports


2011


* Hansen, N.. Research Report RR-7748: Injecting External Solutions Into CMA-ES. INRIA Oct 2011.   Click here to see the bib entry for this publication


* Hansen, N.. Research Report RR-7751: A CMA-ES for Mixed-Integer Nonlinear Optimization. INRIA Oct 2011.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2010


* Fialho, A. and Ros, R.. INRIA Research Report RR-7259: Analysis of Adaptive Strategy Selection within Differential Evolution on the BBOB-2010 Noiseless Benchmark. INRIA Saclay - Ile-de-France April 2010.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2009


* Finck, S. , Hansen, N. , Ros, R. and Auger, A.. Technical report 2009/20: Real-Parameter Black-Box Optimization Benchmarking 2009: Presentation of the Noiseless Functions. Research Center PPE 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Finck, S. , Hansen, N. , Ros, R. and Auger, A.. Technical report 2009/21: Real-Parameter Black-Box Optimization Benchmarking 2009: Presentation of the Noisy Functions. Research Center PPE 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Finck, S. and Ros, R.. Technical report RT-0372: Real-Parameter Black-Box Optimization Benchmarking 2009 Software: User Documentation. INRIA 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N. , Auger, A. , Finck, S. and Ros, R.. Research Report RR-6828: Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup. INRIA 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N. , Auger, A. , Finck, S. and Ros, R.. Technical report RR-6828: Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup. INRIA 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N. , Finck, S. , Ros, R. and Auger, A.. Research Report RR-6829: Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. INRIA 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N. , Finck, S. , Ros, R. and Auger, A.. Technical report RR-6869: Real-Parameter Black-Box Optimization Benchmarking 2009: Noisy Functions Definitions. INRIA 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N. , Finck, S. , Ros, R. and Auger, A.. Technical report RR-6829: Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. INRIA 2009.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


2008


* Bibai, J. , Schoenauer, M. , Savéant, P. and Vidal, V.. Research Report RT-0355: Planification Evolutionnaire par Décomposition. INRIA 2008.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N.. Research Report RR-6518: Adaptive Encoding for Optimization. INRIA 2008.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N.. Research Report RR-6527: CMA-ES with Two-Point Step-Size Adaptation. INRIA 2008.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Hansen, N. , Ros, R. , Mauny, N. , Schoenauer, M. and Auger, A.. Research Report RR-6447: PSO Facing Non-Separable and Ill-Conditioned Problems. INRIA 2008.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication


* Ros, R. and Hansen, N.. Research Report RR-6498: A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity. INRIA 2008.   Click here to see the PDF file or the official link to it  Click here to see the bib entry for this publication