arXiv AI
Big Brains and Changing Environments: Cause or Consequence?
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arXiv AI
2d ago
Multi-Behavioral Evolved Substrates Through Neuromodulation and Activation Selection
The paper investigates whether artificial evolution can replicate biological neuromodulation and diverse neuron types in indirectly encoded substrates. Experiments show that neuromodulation alone cannot overcome a 75% performance ceiling on parity tasks, but combining neuromodulation with per‑task activation function selection allows a single evolving genotype to achieve 100% success across five tasks. This demonstrates that both neuromodulation and evolvable computational primitives are necessary for multi‑behavioral open‑ended evolution.
arXiv Machine Learning
1d ago
Continual Reinforcement Learning with Neuroevolution
The paper investigates continual reinforcement learning using neuroevolution, comparing evolution strategies (ES) and genetic algorithms (GAs) across diverse environments and network sizes. ES consistently achieves a better balance between stability and plasticity, while GAs are more plastic but forget more. The authors attribute this to ES finding wider neighborhoods in weight space, with overlap between consecutive tasks correlating with the stability-plasticity trade‑off, and note that common RL plasticity issues do not transfer to neuroevolution.
arXiv AI
Sep 2
Flawed in Nature, Perfect through Evolution
arXiv:2609.00129v1 Announce Type: cross Abstract: The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a nea...
arXiv AI
Aug 12
Neuroevolution Arena: Nested Ecological Evaluation of Update-and-Inheritance Regimes across Neural Architectures
arXiv:2608. 10323v1 Announce Type: new Abstract: Competitive artificial-life systems can rank trained controllers differently under training and ecological evaluation.
arXiv Machine Learning
Jul 13
Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning
arXiv:2606. 20858v2 Announce Type: replace Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored.
arXiv AI
Sep 3
An Energy-Based Mechanism for Compositional Behavior
The paper proposes a single variational principle that explains how gating mechanisms, their dynamics, and neural implementations for behavioral composition can be unified. This principle yields softmax gating, an energy‑based dynamical system with guaranteed convergence, and a recurrent neural network model with context‑dependent, local interactions. Experiments across collective behavior, human decision‑making, and layered control show that the mechanism reproduces known behavioral patterns, offers interpretable accounts of behavior combination, and matches or outperforms existing methods.