Evolutionary Optimization

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19 papers in the last four weeks, up 90% on the four weeks before. 0.2% of all new papers.

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Operator Calculus for Population-Based Optimization: Modular Convergence and Finite-Population Guarantees
Jun 12, 2026Pekka Malo, Lauri Viitasaari, Patrik Nummi +3Evolutionary Optimization Optimization Convergence Analysis
Aalto University, Department of Information and Service Management, Finland. · Aalto University, Department of Information and Communications Engineering, Finland. · Indian Institute of Management Ahmedabad, Operations and Decision Sciences, India. +1
MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Design
Jun 12, 2026Zishang Qiu, Xinan Chen, Rong Qu +1Automated Heuristic Design Evolutionary Optimization
School of Computer Science, University of Nottingham Ningbo China, Ningbo, China · School of Computer Science, University of Nottingham, Nottingham, UK
Co-Evolved Spiking Neural Network Ensembles via Marginal Contribution Fitness
Jun 12, 2026Catherine Rodriquez, James GhawalyEvolutionary Optimization Ensemble Learning
Louisiana State University Baton Rouge, Louisiana, USA
The $(1 + 1)$ -EA in Dynamic Environments
Jun 11, 2026Georg Hasebe, Johannes Lengler, Raghu Raman RaviEvolutionary Optimization Black-Box Optimization
Eidgenössische Technische Hochschule Zürich, Switzerland
Improved Runtime Bound for the $(μ+ 1)$ EA on BinVal
Jun 11, 2026Joris Belder, Johannes Lengler, Raghu Raman RaviEvolutionary Optimization Black-Box Optimization
ETH Zürich, Switzerland
Runtime Analysis of the $(μ+ 1)$ -ES in a Homogenous Progress Model
Jun 11, 2026Johannes Lengler, Raghu Raman RaviEvolutionary Optimization Evolution Strategies
Eidgenössische Technische Hochschule Zürich, Switzerland
SPEA2 $^+$ : Improved Density Estimation in SPEA2 with Provable Runtime Guarantees
Jun 10, 2026Duc-Cuong Dang, Andre Opris, Dirk SudholtEvolutionary Optimization Multi-Objective Evolutionary Optimization
University of Passau, Passau, Germany
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
Jun 9, 2026Fei Wang, Si Si, Cho-Jui Hsieh +1Evolutionary Optimization LLM Prompting
Google · UCLA
Generation of Diverse and Functional Robot Designs using Superquadrics Parametrisation and Quality-Diversity
Jun 9, 2026Leni Le Goff, Simon Smith, Emma HartEvolutionary Optimization Robotics
Edinburgh Napier University, Scotland UK
Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
Jun 9, 2026Haoran Li, Zengle Ge, Ziyang Zhang +10Game-Playing Agents Evolutionary Optimization
University of Chinese Academy of Sciences · Famou Agent Team, Baidu AI Cloud · The University of Sydney, Australia +3
Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning
Jun 8, 2026Tai Nguyen, Phong Le, Carola Doerr +1Evolutionary Optimization Deep Q-Learning
University of St Andrews, St Andrews, United Kingdom · Sorbonne Universit´e, CNRS, LIP6, Paris, France
Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration
Jun 8, 2026Björn Þór Jónsson, Çağrı Erdem, Stefano Fasciani +1Evolutionary Optimization Quality-Diversity Optimization
1*Department of Informatics, University of Oslo, Norway. · Department of Musicology, University of Oslo, Norway.
Quantitative Performance Analysis of Stopping Criteria for CMA-ES
Jun 8, 2026Ryoji TanabeEarly Stopping Evolutionary Optimization
Yokohama National University, Yokohama, Japan
Gray-Box Optimization and the Vertex Coloring Problem
Jun 6, 2026Johanna Gasse, Antonia Heinen, Hendrik Higl +1Evolutionary Optimization Metaheuristic Optimization
Hasso Plattner Institute University of Potsdam Potsdam, Germany
Rapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists
Jun 5, 2026Ankit Sinha, Nitish Sontakke, Dennis Hong +2Legged Robotics Robot Morphology-Control Co-Design
School of Interactive Computing, Georgia Institute of Technology, TSRB 85 5th St NW, Atlanta, GA - 30332, USA · Dept. of Mechanical and Aerospace Engineering, University of California, Los Angeles, 420 Westwood Plaza, CA 90095, USA · Robotic Systems Lab, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland
MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation
Jun 4, 2026Ilyass Taouil, Michal Ciebelski, Shafeef Omar +4Humanoid Loco-Manipulation Long-Horizon Robotic Manipulation
Technical University of Munich, Germany · New York University, USA · Carnegie Mellon University, USA
Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies
Jun 4, 2026Sichen Wang, Zhipeng LuEvolutionary Optimization Black-Box Optimization
Department of Engineering, Shenzhen MSU-BIT University, Shenzhen, China · Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen, China
Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery
Jun 3, 2026Wenxiao Li, Yongjian Liu, Qing XieEvolutionary Optimization Time Series Classification
School of Computer Science and Artificial Intelligence, Wuhan University of Technology, No. 122 Luoshi Road, Hongshan District, Wuhan, 430070, Hubei, China
LLM-Driven Co-Evolutionary Automated Heuristic Design for Bi-Component Coupled Combinatorial Optimization
May 30, 2026Mingen Kuang, Xudong Deng, Xi Lin +3Automated Heuristic Design Evolutionary Optimization
Xi’an Jiao Tong University · Northwestern Polytechnical University
EvoDefense: Co-Evolving Black-Box Defense with Large Language Models
May 29, 2026Yu Li, Yuenan Hou, Yingmei Wei +2Evolutionary Optimization Adversarial Attacks on LLMs
National University of Defense Technology · Shanghai AI Laboratory
Learning to Solve and Optimize by Evolving Code
May 29, 2026Veronika Semmelrock, Benedetta Strizzolo, Francesco Zuccato +3Automated Algorithm Discovery Evolutionary Optimization
University of Klagenfurt, Austria · University of Udine, Italy
GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis
May 29, 2026Jasper Post, Johannes Koch, Anton Bouter +2GPU Acceleration Evolutionary Optimization
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands · Delft University of Technology, Delft, The Netherlands · Leiden University Medical Center, Leiden, The Netherlands
Overcoming Forgetting in LLM Fine-Tuning with Evolution Strategies
May 28, 2026Kajetan Schweighofer, Conor F. Hayes, Roberto Dailey +2Continual Learning for LLMs Evolutionary Optimization
Cognizant AI Lab · UT Austin & Cognizant AI Lab
Runtime Analysis of a Compact Genetic Algorithm on a Truly Multi-valued OneMax Function
May 28, 2026Martin S. Krejca, Carsten WittStochastic Optimization Evolutionary Optimization
Laboratoire d’Informatique (LIX), CNRS, École Polytechnique, IP Paris, France · DTU Compute, Technical University of Denmark, Denmark
EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization
May 28, 2026Tao Jiang, Xinmeng Yu, Chenhao Yi +5Evolutionary Optimization Language Model Merging
Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology · Pengcheng Laboratory · University of Chinese Academy of Sciences +4
Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits
May 28, 2026Sixue Xing, Haoyu He, Kerui Wu +4Multi-Armed Bandits Evolutionary Optimization
University of Notre Dame · 2Northeastern University · University of Massachusetts Amherst +4
Self-Improving Language Models with Bidirectional Evolutionary Search
May 27, 2026Guowei Xu, Zhenting Qi, Huangyuan Su +4Evolutionary Optimization LLM Planning
1Harvard University · 2MIT
A Fresh Look at Lamarckian Evolution and the Baldwin Effect
May 27, 2026Inès Benito, Johannes F. Lutzeyer, Benjamin DoerrEvolutionary Optimization Combinatorial Optimization
Laboratoire d’Informatique (LIX), CNRS, École Polytechnique, Institut Polytechnique de Paris, France
Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
May 27, 2026Helena Stegherr, Michael Heider, Nils Meyer +7Explainable Artificial Intelligence Evolutionary Optimization
Universität Augsburg, Germany · Universität Augsburg, Augsburg, Germany
TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems
May 27, 2026Yi Ding, Zijie Xuan, Haowei Zhou +6Multi-Agent LLM Systems Multi-Agent System Optimization
National Institute of Metrology, China · University of California, Berkeley · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences +5

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Operator Calculus for Population-Based Optimization: Modular Convergence and Finite-Population Guarantees

Pekka Malo, Lauri Viitasaari, Patrik Nummi, Antti Suominen+2

Aalto University, Department of Information and Service Management, Finland. · Aalto University, Department of Information and Communications Engineering, Finland. · Indian Institute of Management Ahmedabad, Operations and Decision Sciences, India.+1

Evolutionary Optimization Optimization Convergence Analysis Metaheuristic Optimization Nonconvex Optimization Mean-Field Theory

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Population-based optimizers combine update rules such as mutation, selection, and recombination. When one rule changes, it is often unclear which convergence guarantees survive or how the new combination should be assessed. We develop an operator calculus: an operator is a population-update rule, and the calculus specifies how separately checked effects can be combined. Under explicit regularity and small-step conditions, the leading changes caused by the updates add, yielding reusable building blocks for convergence analysis. The framework distinguishes finding and retaining a good solution, reducing the population's mean objective, and concentrating candidates near an optimizer, and identifies the extra approximation conditions needed for finite evaluation-budget guarantees. Applications include distribution adaptation, recombinative evolution, and consensus dynamics, with verified nonconvex cases. Controlled experiments on a common nonconvex problem collection show how component effects change with population geometry and the performance measure: a rule can worsen the mean objective yet produce better candidates.

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