2 citations · 7 across the 14 of their papers we have counts for
16 papers
Continual Reinforcement Learning with Neuroevolution
Eleni Nisioti, Andrea Cossu, Kathrin Korte +1
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 bal…
On Growth and Form, and Function: Reusable Regulatory Handles Control Phenotypic Variation
Benedikt Hartl, Milton L. Montero, Marcello Barylli +2
How phenotypic transformations are implemented by changes in underlying regulatory dynamics remains a central question in developmental biology. Inspired by D'Arcy Thompson's 1917…
Self-Organising Digital Circuits
Marcello Barylli, Gabriel Béna, Alexander Mordvintsev +2
Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes. Biological systems, in contrast, exhibit a…
Dimensionality Controls When Modularity Helps in Continual Learning
Kathrin Korte, Christian Medeiros Adriano, Joachim Winther Pedersen +2
Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tas…
When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry
Kathrin Korte, Joachim Winter Pedersen, Eleni Nisioti +1
To preserve previously learned representations, continual learning systems must strike a balance between plasticity, the ability to acquire new knowledge, and stability. This stabi…
Tournament Informed Adversarial Quality Diversity
Timothée Anne, Noah Syrkis, Meriem Elhosni +5
Quality diversity (QD) is a branch of evolutionary computation that seeks high-quality and behaviorally diverse solutions to a problem. While adversarial problems are common, class…
Hypernetworks That Evolve Themselves
Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti +4
How can neural networks evolve themselves without relying on external optimizers? We propose Self-Referential Graph HyperNetworks, systems where the very machinery of variation and…
When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?
Eleni Nisioti, Joachim Winther Pedersen, Erwan Plantec +2
The ability to continuously and efficiently transfer skills across tasks is a hallmark of biological intelligence and a long-standing goal in artificial systems. Reinforcement lear…
Adversarial Coevolutionary Illumination with Generational Adversarial MAP-Elites
Timothée Anne, Noah Syrkis, Meriem Elhosni +4
Quality-Diversity (QD) algorithms seek to discover diverse, high-performing solutions across a behavior space, in contrast to conventional optimization methods that target a single…
Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots
Binggwong Leung, Worasuchad Haomachai, Joachim Winther Pedersen +2
Artificial neural networks can be used to solve a variety of robotic tasks. However, they risk failing catastrophically when faced with out-of-distribution (OOD) situations. Severa…
Harnessing Language for Coordination: A Framework and Benchmark for LLM-Driven Multi-Agent Control
Timothée Anne, Noah Syrkis, Meriem Elhosni +4
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. Their potential to facilitate human coordination with many agents is a promising but lar…
Collective Innovation in Groups of Large Language Models
Eleni Nisioti, Sebastian Risi, Ida Momennejad +2
Human culture relies on collective innovation: our ability to continuously explore how existing elements in our environment can be combined to create new ones. Language is hypothes…
Meta-Learning an Evolvable Developmental Encoding
Milton L. Montero, Erwan Plantec, Eleni Nisioti +2
Representations for black-box optimisation methods (such as evolutionary algorithms) are traditionally constructed using a delicate manual process. This is in contrast to the repre…
Evolving Self-Assembling Neural Networks: From Spontaneous Activity to Experience-Dependent Learning
Erwan Plantec, Joachin W. Pedersen, Milton L. Montero +2
Biological neural networks are characterized by their high degree of plasticity, a core property that enables the remarkable adaptability of natural organisms. Importantly, this ab…
Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti +2
Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the net…
Growing Artificial Neural Networks for Control: the Role of Neuronal Diversity
Eleni Nisioti, Erwan Plantec, Milton Montero +2
In biological evolution complex neural structures grow from a handful of cellular ingredients. As genomes in nature are bounded in size, this complexity is achieved by a growth pro…