Abstract

Cognitive twins, digital replicas of cognitive processes, have emerged as a transformative approach in artificial intelligence and human-machine collaboration. This paper presents a framework for developing a cognitive twin by integrating a Distributed Cognitive System (DCS) with Evolutionary Strategies (ES). The DCS enables decentralized knowledge processing, while ES optimizes learning and adaptation over time. Our approach is evaluated on real-world datasets, demonstrating its efficiency in cognitive modeling and decision-making. Results highlight improvements in adaptability, scalability, and accuracy compared to traditional AI models.