GP and GA in the design of a constrained control system with disturbance rejection. - Andrea Soltoggio (ISIC, 2004)

A Comparison of Genetic Programming and Genetic Algorithms in the Design of a Robust, Saturated Control System. - Andrea Soltoggio (GECCO (2), 2004)

An enhanced GA to improve the search process reliability in tuning of control systems. - Andrea Soltoggio (GECCO, 2005)

A simple line search operator for ridged landscapes. - Andrea Soltoggio (GECCO, 2006)

Evolving neuromodulatory topologies for reinforcement learning-like problems. - Andrea Soltoggio; Peter Dürr; Claudio Mattiussi; Dario Floreano (IEEE Congress on Evolutionary Computation, 2007)

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios. - Andrea Soltoggio; John A. Bullinaria; Claudio Mattiussi; Peter Dürr; Dario Floreano (ALIFE, 2008)

Neural Plasticity and Minimal Topologies for Reward-Based Learning. - Andrea Soltoggio (HIS, 2008)

Novelty of behaviour as a basis for the neuro-evolution of operant reward learning. - Andrea Soltoggio; Ben Jones (GECCO, 2009)

Evolution of neural symmetry and its coupled alignment to body plan morphology. - Ben Jones; Andrea Soltoggio; Bernhard Sendhoff; Xin Yao 001 (GECCO, 2011)

Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing. - Andrea Soltoggio; Andre Lemme; Jochen J. Steil (ROBIO, 2012)

How Rich Motor Skills Empower Robots at Last: Insights and Progress of the AMARSi Project. - Andrea Soltoggio; Jochen J. Steil (Künstliche Intell., 2012)

From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation. - Andrea Soltoggio; Kenneth O. Stanley (Neural Networks, 2012)

Learning the rules of a game: Neural conditioning in human-robot interaction with delayed rewards. - Andrea Soltoggio; René Felix Reinhart; Andre Lemme; Jochen J. Steil (ICDL-EPIROB, 2013)

Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances. - Andrea Soltoggio; Andre Lemme; René Felix Reinhart; Jochen J. Steil (Frontiers Neurorobotics, 2013)

Movement Primitives as a Robotic Tool to Interpret Trajectories Through Learning-by-doing. - Andrea Soltoggio; Andre Lemme (Int. J. Autom. Comput., 2013)

Solving the Distal Reward Problem with Rare Correlations. - Andrea Soltoggio; Jochen J. Steil (Neural Comput., 2013)

POET: An Evo-Devo Method to Optimize the Weights of Large Artificial Neural Networks. - Alessandro Fontana; Andrea Soltoggio; Borys Wróbel (ALIFE, 2014)

Real-time Hebbian Learning from Autoencoder Features for Control Tasks. - Justin K. Pugh; Andrea Soltoggio; Kenneth O. Stanley (ALIFE, 2014)

Short-term plasticity as cause-effect hypothesis testing in distal reward learning. - Andrea Soltoggio (Biol. Cybern., 2015)

Editorial: Neural plasticity for rich and uncertain robotic information streams. - Andrea Soltoggio; Frank van der Velde (Frontiers Neurorobotics, 2015)

Building Efficient Deep Hebbian Networks for Image Classification Tasks. - Yanis Bahroun; Eugénie Hunsicker; Andrea Soltoggio (ICANN (1), 2017)

Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification. - Yanis Bahroun; Andrea Soltoggio (ICANN (1), 2017)

Neural Networks for Efficient Nonlinear Online Clustering. - Yanis Bahroun; Eugénie Hunsicker; Andrea Soltoggio (ICONIP (1), 2017)

Online Representation Learning with Multi-layer Hebbian Networks for Image Classification Tasks. - Yanis Bahroun; Andrea Soltoggio (CoRR, 2017)

Distributed Strategy Adaptation with a Prediction Function in Multi-Agent Task Allocation. - Joanna Turner; Qinggang Meng; Gerald Schaefer; Andrea Soltoggio (AAMAS, 2018)

Convolutional neural networks for automated targeted analysis of raw gas chromatography-mass spectrometry data. - Angelika Skarysz; Yaser Alkhalifah; Kareen Darnley; Michael Eddleston; Yang Hu 004; Duncan B. McLaren; William H. Nailon; Dahlia Salman; Martin Sykora; C. L. Paul Thomas; Andrea Soltoggio (IJCNN, 2018)

Fast consensus for fully distributed multi-agent task allocation. - Joanna Turner; Qinggang Meng; Gerald Schaefer; Andrea Soltoggio (SAC, 2018)

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks. - Andrea Soltoggio; Kenneth O. Stanley; Sebastian Risi (Neural Networks, 2018)

Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System. - Joanna Turner; Qinggang Meng; Gerald Schaefer; Amanda Whitbrook; Andrea Soltoggio (IEEE Trans. Cybern., 2018)

Understanding Human Behaviour in Industrial Human-Robot Interaction by Means of Virtual Reality. - Piotr Fratczak; Yee Mey Goh; Peter Kinnell; Andrea Soltoggio; Laura Justham (HTTF, 2019)

A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs. - Ling Jiang; Yang Hu 004; Xilin Xia; Qiuhua Liang; Andrea Soltoggio (CoRR, 2019)

Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture. - Pawel Ladosz; Eseoghene Ben-Iwhiwhu; Yang Hu 004; Nicholas Ketz; Soheil Kolouri; Jeffrey L. Krichmar; Praveen K. Pilly; Andrea Soltoggio (CoRR, 2019)

A fully convolutional two-stream fusion network for interactive image segmentation. - Yang Hu 004; Andrea Soltoggio; Russell Lock; Steve Carter (Neural Networks, 2019)

Evolving inborn knowledge for fast adaptation in dynamic POMDP problems. - Eseoghene Ben-Iwhiwhu; Pawel Ladosz; Jeffery Dick; Wen-Hua Chen 001; Praveen K. Pilly; Andrea Soltoggio (GECCO, 2020)

Virtual Reality Study of Human Adaptability in Industrial Human-Robot Collaboration. - Piotr Fratczak; Yee Mey Goh; Peter Kinnell; Laura Justham; Andrea Soltoggio (ICHMS, 2020)

Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations. - Soheil Kolouri; Nicholas A. Ketz; Andrea Soltoggio; Praveen K. Pilly (ICLR, 2020)

Fast and automated biomarker detection in breath samples with machine learning. - Angelika Skarysz; Dahlia Salman; Michael Eddleston; Martin Sykora; Eugénie Hunsicker; William H. Nailon; Kareen Darnley; Duncan B. McLaren; C. L. Paul Thomas; Andrea Soltoggio (CoRR, 2020)

Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable Environments. - Jeffery Dick; Pawel Ladosz; Eseoghene Ben-Iwhiwhu; Hideyasu Shimadzu; Peter Kinnell; Praveen K. Pilly; Soheil Kolouri; Andrea Soltoggio (Frontiers Neurorobotics, 2020)

Improving the segmentation of scanning probe microscope images using convolutional neural networks. - Steff Farley; Jo E. A. Hodgkinson; Oliver M. Gordon; Joanna Turner; Andrea Soltoggio; Philip J. Moriarty; Eugénie Hunsicker (Mach. Learn. Sci. Technol., 2021)

Flexible Path Planning in a Spiking Model of Replay and Vicarious Trial and Error. - Jeffrey L. Krichmar; Nicholas A. Ketz; Praveen K. Pilly; Andrea Soltoggio (SAB, 2022)

Biological underpinnings for lifelong learning machines. - Dhireesha Kudithipudi; Mario Aguilar-Simon; Jonathan Babb; Maxim Bazhenov; Douglas Blackiston; Josh C. Bongard; Andrew P. Brna; Suraj Chakravarthi Raja; Nick Cheney; Jeff Clune; Anurag Reddy Daram; Stefano Fusi; et al. (Nat. Mach. Intell., 2022)

Context meta-reinforcement learning via neuromodulation. - Eseoghene Ben-Iwhiwhu; Jeffery Dick; Nicholas A. Ketz; Praveen K. Pilly; Andrea Soltoggio (Neural Networks, 2022)

Deep Reinforcement Learning With Modulated Hebbian Plus Q-Network Architecture. - Pawel Ladosz; Eseoghene Ben-Iwhiwhu; Jeffery Dick; Nicholas Ketz; Soheil Kolouri; Jeffrey L. Krichmar; Praveen K. Pilly; Andrea Soltoggio (IEEE Trans. Neural Networks Learn. Syst., 2022)

Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks. - Saptarshi Nath; Christos Peridis; Eseoghene Ben-Iwhiwhu; Xinran Liu; Shirin Dora; Cong Liu; Soheil Kolouri; Andrea Soltoggio (CoLLAs, 2023)

$ mathrm{R}^{3}$: On-Device Real-Time Deep Reinforcement Learning for Autonomous Robotics. - Zexin Li 001; Aritra Samanta; Yufei Li 001; Andrea Soltoggio; Hyoseung Kim 001; Cong Liu 005 (RTSS, 2023)

The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning. - Andrea Soltoggio; Eseoghene Ben-Iwhiwhu; Christos Peridis; Pawel Ladosz; Jeffery Dick; Praveen K. Pilly; Soheil Kolouri (CoRR, 2023)

R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics. - Zexin Li 001; Aritra Samanta; Yufei Li 001; Andrea Soltoggio; Hyoseung Kim 001; Cong Liu 005 (CoRR, 2023)

Lifelong Reinforcement Learning with Modulating Masks. - Eseoghene Ben-Iwhiwhu; Saptarshi Nath; Praveen K. Pilly; Soheil Kolouri; Andrea Soltoggio (Trans. Mach. Learn. Res., 2023)

A domain-agnostic approach for characterization of lifelong learning systems. - Megan M. Baker; Alexander New; Mario Aguilar-Simon; Ziad Al-Halah; Sébastien M. R. Arnold; Eseoghene Ben-Iwhiwhu; Andrew P. Brna; Ethan Brooks; Ryan C. Brown; Zachary Daniels; Anurag Reddy Daram; Fabien Delattre; et al. (Neural Networks, 2023)

Statistical Context Detection for Deep Lifelong Reinforcement Learning. - Jeffery Dick; Saptarshi Nath; Christos Peridis; Eseoghene Ben-Iwhiwhu; Soheil Kolouri; Andrea Soltoggio (CoLLAs, 2024)

SLoSH: Set Locality Sensitive Hashing via Sliced-Wasserstein Embeddings. - Yuzhe Lu; Xinran Liu; Andrea Soltoggio; Soheil Kolouri (WACV, 2024)

Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks. - Dylan Adams; Magda Zajaczkowska; Ashiq Anjum; Andrea Soltoggio; Shirin Dora (CoRR, 2024)

A collective AI via lifelong learning and sharing at the edge. - Andrea Soltoggio; Eseoghene Ben-Iwhiwhu; Vladimir Braverman; Eric Eaton; Benjamin Epstein; Yunhao Ge; Lucy Halperin; Jonathan P. How; Laurent Itti; Michael A. Jacobs; Pavan Kantharaju; Long Le; et al. (Nat. Mac. Intell., 2024)

Wasserstein task embedding for measuring task similarities. - Xinran Liu; Yikun Bai; Yuzhe Lu; Andrea Soltoggio; Soheil Kolouri (Neural Networks, 2025)

Collaborative Learning in Agentic Systems: A Collective AI is Greater Than the Sum of Its Parts. - Saptarshi Nath; Christos Peridis; Eseoghene Ben-Iwhiwhu; Xinran Liu; Soheil Kolouri; Peter Kinnell; Zexin Li; Cong Liu; Shirin Dora; Andrea Soltoggio (CoRR, 2025)

Design of electric powertrains to achieve NVH performance using autoencoders and a physical meaningful latent space. - Marcos Ricardo Souza; Günter Offner; Andrea Soltoggio; Mahdi Mohammadpour; Stephanos Theodossiades (Neural Comput. Appl., 2025)