PeopleofSuperintelligence

Researcher

Nikolaus Hansen

Nikolaus Hansen is an optimization researcher whose best-known contribution to evolutionary computation is Covariance Matrix Adaptation (CMA), which gives the CMA evolution strategy (CMA-ES) for black-box optimization its name. Educated in medicine and mathematics, he earned a PhD in civil engineering at the Technical University Berlin in 1998 under Ingo Rechenberg; before joining Inria, he worked in evolutionary computation, genomics and computational science at that university, the Institute of Genetic Medicine and ETH Zurich. A 2001 paper written with Ostermeier on derandomized self-adaptation in evolution strategies appeared in Evolutionary Computation, and he completed a habilitation at University Paris-Sud in 2010. With Anne Auger, Dimo Brockhoff and others, he introduced the COCO platform for comparing continuous optimizers in 2016, and the 2010 paper comparing 31 algorithms on BBOB-2009, which he coauthored, received the 2020 SIGEVO Impact Award. He is a research director at Inria and works in the RandOpt group at the Centre for Applied Mathematics (CMAP) of École polytechnique.

Now at
Inria and Centre for Applied Mathematics of École polytechnique

Timeline

  1. 1992

    A German neural networks book

    The third, expanded edition of Neuronale Netzwerke: Einführung, Überblick und Anwendungsmöglichkeiten, which he writes with E. Schöneburg and A. Gawelczyk, appears in 1992 from Markt&Technik Verlag in Haar.

  2. 1998

    Doctorate at Technical University Berlin

    Educated in medicine and mathematics, he earns a PhD in civil engineering from the Technical University Berlin in 1998 under Ingo Rechenberg, with a thesis on generalized individual step-size control in evolution strategies. Before joining Inria, he works in evolutionary computation, genomics and computational science at the Technical University Berlin, the Institute of Genetic Medicine and ETH Zurich.

  3. 2001–2003

    Derandomized evolution strategy papers

    In 2001, he and Ostermeier publish “Completely derandomized self-adaptation in evolution strategies” in Evolutionary Computation, and in 2003 he publishes “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)” with S. D. Müller and P. Koumoutsakos. His best-known contribution to evolutionary computation is Covariance Matrix Adaptation, and CMA-ES is a stochastic method for optimizing non-linear, non-convex functions in continuous domain.

  4. 2010

    Habilitation at Paris-Sud

    He completes a habilitation to direct research at University Paris-Sud in 2010, with the work Variable Metrics in Evolutionary Computation.

  5. 2011

    Information-geometric optimization paper

    In 2011, he coauthors with Yann Ollivier, Ludovic Arnold and Anne Auger a paper that turns any smooth parametric family of probability distributions into a black-box optimization method, the information-geometric optimization (IGO) method. Its IGO flow conducts natural gradient ascent of an adaptive, quantile-based transformation of the objective function.

  6. 2016

    COCO platform and CMA tutorial

    In 2016, he posts “COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting” on arXiv with Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar and Dimo Brockhoff; the open source platform automates the benchmarking of numerical optimization algorithms. The CMA Evolution Strategy: A Tutorial, which he posts on arXiv in 2016, introduces CMA-ES and derives the algorithm from intuitive concepts.

  7. 2020

    A SIGEVO Impact Award

    In 2020, the paper “Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009”, by him, Anne Auger, Raymond Ros, Steffen Finck and Petr Pošík and published at GECCO 2010, receives the SIGEVO Impact Award for ten-year impact, announced at the ACM GECCO 2020 conference.

  8. As of October 2026

    Research director at Inria

    He is a directeur de recherche at Inria, based at the Saclay research centre, and works in the Randomised Optimisation group (RandOpt) at the Centre for Applied Mathematics (CMAP) of École polytechnique. RandOpt is a project team joint with Inria Saclay–Île-de-France, headed by Anne Auger.

Career

  1. NowInriaResearch Director
  2. NowCentre for Applied Mathematics of École polytechnique

Education

  1. 2010Université Paris-SudHabilitation to direct research
  2. 1998Technical University BerlinPhD in civil engineering

From the biography.

  1. People shaping AI work at a median of 3 organizations over a career: 3 for university starters and institute and lab starters, versus 2 for company startersIncludes Inria. Across 6,853 people shaping AI with a dated work role, we estimate a median of 3 parent organizations over a career (mean 3.6), comparing role groups, starting sectors and starting decades from the 1950s to 2020–2026.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Inria. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. Google has the highest combined h-index among AI organizationsIncludes Inria. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  4. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Inria. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.

Sources and credits

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  1. Publications - CMAP cmap.polytechnique.fr. Checked 22 October 2026.
  2. GECCO 2022 | Tutorials gecco-2022.sigevo.org. Checked 22 October 2026.
  3. CMA-ES - Wikipedia en.wikipedia.org. Checked 22 October 2026.
  4. The CMA Evolution Strategy: A Tutorial arxiv.org. Checked 22 October 2026.
  5. Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles arxiv.org. Checked 22 October 2026.
  6. COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting arxiv.org. Checked 22 October 2026.
  7. SIGEVO Impact Award sig.sigevo.org. Checked 22 October 2026.
  8. SIGEvolution Volume 13, Issue 4 evolution.sigevo.hosting.acm.org. Checked 22 October 2026.
  9. Homepage of Nikolaus Hansen cmap.polytechnique.fr. Checked 22 October 2026.
  10. RandOpt | Center for Applied Mathematics – Ecole Polytechnique cmap.ip-paris.fr. Checked 22 October 2026.