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This research focused on the application of Genetic Algorithm (GA) based methodologies to collaborative problem solving. Direct mechanisms for online asynchronous group work were compared to an indirect constrained group interaction model which was modeled after recombination used in traditional roulette wheel style GAs. This new methodology contributes to the existing literature by extending the work in the areas of Interactive Evolutionary Computation (IEC) and Human Based Genetic Algorithms (HBGA) by creating a hybrid approach, Indirect Collaborative Evolution (ICE), employing some of the methodological features found within IEC with the collaborative human-centric features of HBGA. This contribution is important as it presents a new means of facilitating group intelligence through indirect interactions (i.e., cold collaboration) and provides new tools to collaborative rich fields, such as Nursing.

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Daniel holds advanced degrees in Cognitive Psychology, Nursing, and Systems Science from Binghamton University and is a User Experience designer in the Healthcare IT domain. Daniel also serves as an Adjunct Professor in the Decker School of Nursing at Binghamton University where he teaches various courses in information technology in Healthcare.

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