Product Design
indirect collaborative evolution (ICE)
“ICE,” represents a paradigm shift in Human-Based Genetic Algorithms. Inspired by Interactive Evolutionary Computation, it introduces an innovative approach aimed at enhancing online collaboration. Through rigorous testing, design, and development, ICE opens up new frontiers in augmenting teamwork, promising a future where collaboration is smarter, more efficient, and truly transformative.
Abstract
This research explored computer-aided group organization for collaborative problem-solving in nursing. We compared standard online tools to a novel method using Genetic Algorithms for nursing care plan development.
Our hybrid approach, combining Indirect Collaborative Evolution and Human-Based Genetic Algorithms, introduces a fresh way to boost group intelligence through indirect interactions, especially in fields like nursing.
We analyzed groups based on solution quality, tool usage, group size, and participant satisfaction. The results showed that Indirect Collaborative Evolution performed similarly to the direct method but outperformed in terms of group satisfaction and efficacy. This validates it as a promising approach for collaborative problem-solving in nursing.
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