Covariance Matrix Adaptation Evolution Strategy

Also known as CMA-ES

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Overview

CMA-ES optimizes continuous black-box problems by sampling candidate solutions from a multivariate Gaussian and adapting its covariance and step size toward better candidates. It is useful when gradients are unavailable or unreliable, including expensive simulations, robot design, and other settings where each evaluation has a cost. Common extensions use adaptive restarts and population-size schedules, surrogate models, or gradient and structural information to improve search efficiency and reliability; performance is often assessed on CEC and BBOB benchmark suites. A continuing challenge is that results depend on problem structure, dimension, noise, and evaluation budget, so gains from a variant or stopping rule may not transfer across function classes.

Latest papers

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13 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Hybrid metaheuristics for architectural design optimization under small evaluation and wall-clock budgets

    Oct 19, 2026Luciano AmbrosiniMetaheuristic OptimizationBlack-Box Optimization

    LA Architecture & Computational Design Consulting, Naples, Italy

  2. The Modular CMA-ES: A Framework for Modern Evolution Strategies

    Oct 7, 2026Jacob de Nobel, Diederick Vermetten, Anna V. Kononova +2Evolution StrategiesCovariance Matrix Adaptation Evolution Strategy

  3. LESS: Lightweight Evolutionary Supernet Search in Minutes

    Oct 1, 2026Aviral Gandhi, Jinglue Xu, Jialong Li +1Evolutionary OptimizationCovariance Matrix Adaptation Evolution Strategy

    The University of Tokyo · Sakana AI · Waseda University

  4. Parameter-Free Zeroth-Order Optimization with Ellipsoidal Sampling

    Sep 29, 2026Alexander Sholokhov, Alexander RogozinZeroth-Order OptimizationCovariance Matrix Adaptation Evolution Strategy

    Moscow Institute of Physics and Technology, Russia

  5. Artificial Neural Networks as Surrogate Models in Black Box Optimization

    Sep 16, 2026Md Khadimul Islam Zim, Martin HoleňaSurrogate-Assisted OptimizationBlack-Box Optimization

    Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic · Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, Prague, Czech Republic

  6. SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity

    Jun 20, 2026Yulun Zhuang, Yue Qin, Justin Lu +4Legged RoboticsSurrogate-Assisted Optimization

    Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville, TN 37996, USA

  7. Evolutionary Bilevel Reward Shaping for Generalization in Reinforcement Learning

    Jun 15, 2026Ekasit Usaratniwart, Xilin Gao, Marc Ong +1Reinforcement LearningReward Shaping

    University of Tsukuba, Tsukuba, Japan · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

  8. MSC-CMA-ES: Structure-Aware Restarts for CMA-ES via Cyclic Nearest-Better Basin Discovery

    Jun 14, 2026Dimitar Nedanovski, Svetoslav Nenov, Dimitar PilevEvolutionary OptimizationCovariance Matrix Adaptation Evolution Strategy

    Faculty of Mathematics and Informatics Sofia University St. Kliment Ohridski James Baucher Blvd., 1164 Sofia, Bulgaria · Department of Mathematics University of Chemical Technology and Metallurgy Sofia, Bulgaria · Department of Informatics University of Chemical Technology and Metallurgy Sofia, Bulgaria

  9. Quantitative Performance Analysis of Stopping Criteria for CMA-ES

    Jun 8, 2026Ryoji TanabeEarly StoppingEvolutionary Optimization

    Yokohama National University, Yokohama, Japan

  10. RCMAES: A Robust CMA-ES Variant for CEC2026 Competition

    Apr 29, 2026Khoirul Faiq Muzakka, Sören Möller, Martin FinsterbuschEvolutionary OptimizationBlack-Box Optimization

    Institute of Energy Materials and Devices (IMD-2), Forschungszentrum Jülich GmbH, Germany

  11. Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch

    Mar 25, 2026Fabio Ferreira, Lucca Wobbe, Arjun Krishnakumar +2Hyperparameter OptimizationLarge Language Model-Guided Optimization

    ELLIS Institute Tübingen · University of Freiburg · Karlsruhe Institute of Technology +1

  12. Real-time Rendering-based Surgical Instrument Tracking via Evolutionary Optimization

    Mar 12, 2026Hanyang Hu, Zekai Liang, Florian Richter +1Visual Object TrackingCovariance Matrix Adaptation Evolution Strategy

    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093 USA.

  13. Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG

    Mar 9, 2026Nathaniel Dennler, Zhonghao Shi, Yiran Tao +3Active Preference LearningPreference Learning

    CSAIL/AeroAstro, Massachusetts Institute of Technology. · Department of Computer Science, University of Southern California.