Neuroevolution

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5 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Continual Reinforcement Learning with Neuroevolution
Oct 1, 2026Eleni Nisioti, Andrea Cossu, Kathrin Korte +1Neuroevolution Plasticity
IT University of Copenhagen · University of Pisa · Sakana AI
Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling
Sep 30, 2026Ruoyu Zhao, Jiaqi Wu, Chenyu Zhu +1Spiking Neural Networks Neural Architecture Search
Department of Computer Science, City University of Hong Kong
Environmental requirements for the use of social information by artificial life agents using evolved plastic artificial neural networks
Sep 28, 2026Hugh Charterton, James M. Borg, Aniko EkartNeuroevolution Self-Evolving Agents
Aston Centre for AI Research and Application, Aston University, Birmingham, United Kingdom
Scenario-Driven Neuroevolution: Using Models to Guide Test Generation for Games
Sep 23, 2026Gijs van Cuyck, Patric Feldmeier, Jan Tretmans +1Test Generation Neuroevolution
Radboud University, Institute iCIS, Nijmegen, The Netherlands · University of Passau, Germany · TNO-ESI, Eindhoven, The Netherlands
Self-Replicating Neural Cellular Automata: Quantifying Emergent Phenotypic and Genotypic Diversity in an OpenEnded Substrate
Sep 17, 2026Sanyam Jain, Felix Simon Reimers, Stefano NicheleNeural Cellular Automata Self-Organization
Department of Computer Science and Communication, Østfold University of Applied Sciences, B R A veien 4, 1757 Halden, Norway · Department of Computer Science, Oslo Metropolitan University, Pilestredet 35, 0166 Oslo, Norway
Bio-Inspired Palette Evolution in Indirectly Encoded Substrates: Timescale Compatibility Shapes Activation Function Discovery
Sep 15, 2026Romain Claret, Michael O'Neill, Paul Cotofrei +1Neuroevolution Bio-Inspired Framework
University of Neuchâtel, Neuchâtel, Switzerland · University College Dublin, Ireland
Big Brains and Changing Environments: Cause or Consequence?
Sep 14, 2026Sian Heesom-Green, Jonathan Shock, Geoff NitschkeNeuroevolution Evolutionary Optimization Methods
Department of Computer Science, University of Cape Town Cape Town, South Africa
Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution
Sep 10, 2026Romain Claret, Arthur Gygax, Michael O'Neill +3Neuroevolution Mnist
University of Neuchâtel, Neuchâtel, Switzerland · University College Dublin, Ireland
Neuroevolution Arena: Nested Ecological Evaluation of Update-and-Inheritance Regimes across Neural Architectures
Aug 10, 2026Yuxu Ge, Yifei ChengNeuroevolution Neural Architecture Search
University of York, United Kingdom
SPECS: Speciated Evolutionary Circuit Synthesis
Jul 15, 2026Yağız Gençer, Stefan Uhlich, Andrea Bonetti +3Analog Design Neuroevolution
Sony Group Corporation, Switzerland · TU Munich, Germany
Contravariance Theory: Strong Alignment for Minimal Solutions to Hard Tasks
Jul 9, 2026Dan Yamins, Aran NayebiNeuroevolution Human Brain Networks
Departments of Computer Science and Psychology, and Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305 USA · Machine Learning Department, Neuroscience Institute, and Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA
Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach
Jun 30, 2026George Stamatelis, Hui Chen, Henk Henk Wymeersch +1Reconfigurable Intelligent Surfaces Power Allocation
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis Ilissia, 16122 Athens, Greece · Department of Electrical Engineering, Chalmers University of Technology, 412 58 Gothenburg, Sweden
Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity
Jun 18, 2026Shishen Lin, Yixin ChenNeuroevolution Learning-Based Control
Department of Statistics, University of Warwick, Coventry, United Kingdom · 2Biozentrum, University of Basel, Basel, Switzerland
Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks
Jun 18, 2026Haocheng Duan, Yuxin Guo, Jieyi Bi +4Neural Combinatorial Optimization Concept Bottleneck Models
1Carnegie Mellon University · 2Nanyang Technological University · 3Microsoft Research +1
Co-Evolved Spiking Neural Network Ensembles via Marginal Contribution Fitness
Jun 12, 2026Catherine Rodriquez, James GhawalySpiking Neural Networks Neuroevolution
Louisiana State University Baton Rouge, Louisiana, USA
Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery
Jun 3, 2026Wenxiao Li, Yongjian Liu, Qing XieNeuroevolution Neural Architecture Search
School of Computer Science and Artificial Intelligence, Wuhan University of Technology, No. 122 Luoshi Road, Hongshan District, Wuhan, 430070, Hubei, China
Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
May 13, 2026Johnson Zhou, Daniel Tanneberg, Forough Habibollahi +12Neuromorphic Computing Neuroevolution
Cortical Labs, Australia · Honda Research Institute Europe, Germany
Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
May 10, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuLarge Language Models(Llms Neuroevolution
The University of Hong Kong, China · Stellaris AI Limited, China
Evolutionary feature selection for spiking neural network pattern classifiers
Apr 29, 2026Michal Valko, Nuno C. Marques, Marco CastelaniSpiking Neural Networks Neural Network
Department of Applied Informatics, Comenius University, Mlynská Dolina, 842 48 Bratislava, Slovakia · CENTRIA,Departmento de Informatica, Faculdade de Ciencias e Tecnologia, University Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal · CENTRIA, Faculdade de Ciencias e Tecnologia, University Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal
L-System Genetic Encoding for Scalable Neural Network Evolution: A Comparison with Direct Matrix Encoding
Apr 23, 2026Alexander Stuy, Nodin WeddingtonNeuroevolution Neural Network
Florida State University
Neuro-evolutionary stochastic architectures in gauge-covariant neural fields
Apr 22, 2026Rodrigo Carmo TerinNeuroevolution Neural Fields
King Juan Carlos University, Facuty of Experimental Sciences and Technology, Department of Applied Physics, Av. del Alcalde de M´ostoles, 28933, Madrid, Spain
NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning
Apr 16, 2026Hibatallah Meliani, Khadija Slimani, Samira KhouljiNeuroevolution Path Planning
ISI Laboratory, National School of Applied Sciences (ENSA), Abdelmalek Essaadi University Tetouan, Morocco · EsieaLab LDR, Higher School of Computer Science, Electronics and Automation (ESIEA) Paris, France
NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
Feb 13, 2026Guoan Wang, Shihao Yang, Feng LiuSmall-Sample Electroencephalogram Datasets Electroencephalography
Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Road, Piscataway, NJ 08854, USA
Quantitative Understanding of PDF Fits and their Uncertainties
Dec 30, 2025Amedeo Chiefa, Luigi Del Debbio, Richard KenwayNeural Tangent Kernel Parametric Physics-Informed Neural Network
The Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom
Investigating Hyperparameter Optimization and Transferability for ES-HyperNEAT: A TPE Approach
Date pendingRomain Claret, Michael O'Neill, Paul Cotofrei +1Neuroevolution General Grid Search Framework
University of Neuchâtel Neuchâtel, Switzerland · University College Dublin Dublin, Ireland

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Continual Reinforcement Learning with Neuroevolution

Eleni Nisioti, Andrea Cossu, Kathrin Korte, Sebastian Risi

IT University of Copenhagen · University of Pisa · Sakana AI

Neuroevolution Plasticity Replay-Based Continual Learning Offline Reinforcement Learning

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Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continual task changes, no RL method has yet consistently achieved a good balance between adaptation and forgetting. Here we turn to an alternative optimization paradigm, neuroevolution (NE): algorithms that search directly in weight space through mutation and selection over a population of neural networks. Across a wide array of environments and environmental changes, with policies ranging from a few hundred parameters to million-parameter networks, we compare evolution strategies (ES) and genetic algorithms (GAs) against state-of-the-art continual RL variants and population-based RL. ES most consistently achieves a good stability-plasticity trade-off, while the GA is the most plastic method but forgets more than ES. To explain this, we study the return landscape around each method's solutions. ES finds the widest neighborhoods, i.e.\ regions of weight space in which perturbed policies still solve the task, and the size of the overlap between the neighborhoods of consecutive tasks correlates with a method's stability-plasticity trade-off. Rewarding behavioral diversity in a GA through novelty search makes the population even more plastic, at the cost of forgetting. Finally, symptoms of plasticity loss commonly reported in RL do not transfer to NE. Overall, these results establish NE as a competitive alternative to RL under continual task changes, and suggest that training under perturbations in weight space may be a useful mechanism for continual learning more broadly.

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