Large brains are metabolically costly, and associations with changing environments do not imply they evolved there, as the Cognitive Buffer Hypothesis (CBH) would suggest. They may instead evolve in stable conditions and later facilitate colonization of changing environments. Using neuro-evolution in an artificial seasonal foraging task, we compared agents evolving exclusively in changing environments to agents first evolved in static environments before transitioning. Results show that larger neural networks in dynamic environments arise mainly from prior static evolution, achieving superior performance under unpredictable changes. Our results challenge strict CBH predictions, provide agent-based (computational) support for a colonization-based account and highlight the role of evolutionary history in brain size evolution.
Sian Heesom-Green, Jonathan P. Shock, Geoff S. Nitschke· Proceedings of the Genetic a...· 0 citations
Adversarial robustness remains a significant challenge in machine-learning malware detection. We propose a co-evolutionary adversarial training framework that integrates gradient-based adversarial attacks with evolutionary controllers to improve robustness. A hybrid Convolutional Neural Network (CNN-MLP) processes spatial and vector-based features, while evolved controllers adaptively guide Projected Gradient Descent (PGD) using gradient statistics, optimizing adversarial effectiveness while respecting feature constraints. This online, co-evolutionary process exposes the classifier to increasingly adaptive attacks. Experimental results demonstrate that the proposed approach enhances adversarial robustness relative to standard training while maintaining clean accuracy, exemplifying a co-evolutionary arms-race framework for realistic, feature-constrained malware detection.
Sabre Didi, Geoff S. Nitschke· Proceedings of the Genetic a...· 0 citations