This study adds to our understanding of how teams learn and change, showing that innovation can take the form of an iterative, ongoing process in which bits of K&I are assembled from multiple sources into potential solutions that are then tested.
One challenge is the large number of potential solutions with multiple routes having many frequency options.
These potential solutions may involve multiple sectors outside the traditional health system, thus recognizing the multiple determinants that influence population health status.
The problem is complex and nonlinear and involves a large number of variables or potential solutions or has multiple objectives.
In general, natural computing approaches should be considered when: The problem is complex and nonlinear and involves a large number of variables or potential solutions or has multiple objectives.
The software outputs a crop allocation solution with a crop for every field for every year, respecting all user-defined constraints; the range of potential solutions can then be explored through multiple model runs.
In this paper we have explained how and why we gathered and then synthesised evidence from multiple sources, in order to understand the problems and generate potential solutions.
GAs seek to evolve a population of potential solutions by crossover and mutation, and through multiple generations, the entire population will eventually "evolve" towards a global optimum (a "fitness score").
