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Computer Science

D-Index
39
Citations
12255
World Ranking
9514
National Ranking
230

Overview

Anne Auger is affiliated with École Polytechnique in France and has contributed extensively to the fields of Computer Science and Engineering. Their research spans several subfields, including Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering, Statistics, Probability and Uncertainty, and Civil and Structural Engineering.

Their main research topics include Probabilistic and Robust Engineering Design, Advanced Multi-Objective Optimization Algorithms, Control Systems and Identification, Metaheuristic Optimization Algorithms Research, Neural Networks and Applications, Advanced Adaptive Filtering Techniques, and Stochastic Processes and Financial Applications.

Anne Auger has published papers in various venues, with frequent contributions to:

  • Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • arXiv (Cornell University)
  • Proceedings of the Genetic and Evolutionary Computation Conference
  • Stochastic Processes and their Applications
  • ACM SIGEVOlution

Selected recent publications include:

  • "An ODE method to prove the geometric convergence of adaptive stochastic algorithms", 2021, Stochastic Processes and their Applications
  • "A SIGEVO impact award for a paper arising from the COCO platform", 2021, ACM SIGEVOlution
  • "Scaling-invariant functions versus positively homogeneous functions", 2021, arXiv (Cornell University)
  • "Benchmarking", 2021, Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • "Preface to the Special Issue on Theory of Genetic and Evolutionary Computation", 2021, Algorithmica

Collaborations with other researchers characterize Anne Auger's work. Frequent co-authors include Nikolaus Hansen, Armand Gissler, Dimo Brockhoff, Alexandre Chotard, and Youhei Akimoto.

Best Publications

  • Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization

    P. N. Suganthan;N. Hansen;J. J. Liang;K. Deb

  • A restart CMA evolution strategy with increasing population size

    A. Auger;N. Hansen

  • Markov chain analysis of cumulative step-size adaptation on a linear constrained problem

    Alexandre Chotard;Anne Auger;Nikolaus Hansen

  • Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup

    Nikolaus Hansen;Anne Auger;Raymond Ros

  • Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions

    Nikolaus Hansen;Raymond Ros;Anne Auger

  • Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009

    Nikolaus Hansen;Anne Auger;Raymond Ros;Steffen Finck

  • COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting

    Nikolaus Hansen;Anne Auger;Olaf Mersmann;Tea Tusar

  • Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point

    Anne Auger;Johannes Bader;Dimo Brockhoff;Eckart Zitzler

  • Theory of Randomized Search Heuristics: Foundations and Recent Developments

    Anne Auger;Benjamin Doerr

  • Performance evaluation of an advanced local search evolutionary algorithm

    A. Auger;N. Hansen

  • Evolution Strategies

    Unknown

  • Information-geometric optimization algorithms: a unifying picture via invariance principles

    Yann Ollivier;Ludovic Arnold;Anne Auger;Nikolaus Hansen

  • Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications

    Anne Auger;Johannes Bader;Dimo Brockhoff;Eckart Zitzler

  • COCO: a platform for comparing continuous optimizers in a black-box setting

    Nikolaus Hansen;Anne Auger;Raymond Ros;Olaf Mersmann

  • Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains

    Anne Auger

  • Impacts of invariance in search: When CMA-ES and PSO face ill-conditioned and non-separable problems

    Nikolaus Hansen;Raymond Ros;Nikolas Mauny;Marc Schoenauer

  • Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models

    Zyed Bouzarkouna;Zyed Bouzarkouna;Didier Yu Ding;Anne Auger

  • R-leaping: Accelerating the stochastic simulation algorithm by reaction leaps

    Anne Auger;Philippe Chatelain;Petros Koumoutsakos

  • Principled Design of Continuous Stochastic Search: From Theory to Practice

    Nikolaus Hansen;Anne Auger

  • Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms

    Anne Auger;Olivier Teytaud

  • Theory of Randomized Search Heuristics

    Anne Auger;Carsten Witt

  • Experimental Comparisons of Derivative Free Optimization Algorithms

    A. Auger;N. Hansen;J. M. Perez Zerpa;R. Ros

Frequent Co-Authors

Nikolaus Hansen
Nikolaus Hansen École Polytechnique
Marc Schoenauer
Marc Schoenauer French Institute for Research in Computer Science and Automation - INRIA
Eckart Zitzler
Eckart Zitzler Lucerne University of Applied Sciences and Arts
Carsten Witt
Carsten Witt Technical University of Denmark
Petros Koumoutsakos
Petros Koumoutsakos Harvard University
Benjamin Doerr
Benjamin Doerr École Polytechnique
Gabriela Ochoa
Gabriela Ochoa University of Stirling
Mike Preuss
Mike Preuss Leiden University
L. Darrell Whitley
L. Darrell Whitley Colorado State University

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