The trade-off between accuracy, speed and power consumption are explored with the choice of problem resolution ultimately being characterised by a desired accuracy, flow speed and endurance of a given simulation.
In this theoretical framework, the choice of problem situations is fundamental in order to allow pupils to make hypotheses, to mobilize their knowledge, to argue, and finally, to construct new knowledge.
The above benchmark problems and the discussions of the choice of test problems for an algorithm can be found at http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume24/ortizboyer05a-html/node6.html.
As highlighted by Harman [15], there are only two key ingredients for the application of search-based optimization to software engineering problems: The choice of the problem representation amenable to symbolic manipulation The definition of the fitness function to characterize what is considered to be a good solution.
Because the choice of clinical problem situations for the cases was arbitrary, some problem situations may well have been left out.
The choice of the problem representation amenable to symbolic manipulation.
But the choice of a problem can be daunting when you're just starting out.
The choice of a problem solving mechanism can be made independently of domain analysis.
In particular, the choice of optimization problem, i.e. whether we used maximum likelihood or maximum parsimony, had the largest impact on the final accuracy, with methods that used maximum likelihood (CA-ML, wMRP-ML, and MRP-ML) as a group more accurate than the methods based upon maximum parsimony (CA-MP, wMRP-MP, and MRP-MP).
Therefore, it is impractical in view of the great number of types of finite elements and the large variety of technical problems, requiring the choice of one or more problem-specific elements.
