By Fabio Perez Marzullo, Rodrigo Novo Porto (auth.), Roger Lee, Haeng-Kon Kim (eds.)
The objective of the seventh IEEE/ACIS overseas convention on computing device and data technological know-how (ICIS 2008) and the 2nd IEEE/ACIS foreign Workshop on e-Activity (IWEA 2008) to be hung on may well 14 – sixteen, 2008 in Portland, Oregon, U.S.A. is to compile scientists, engineers, machine clients, and scholars to percentage their reviews and alternate new principles and examine effects approximately all elements (theory, purposes and instruments) of laptop and knowledge technological know-how; and to debate the sensible demanding situations encountered alongside the way in which and the strategies followed to resolve them.
The convention organizers chosen 23 remarkable papers from ICIS/IWEA 2008, all of which you'll locate during this quantity of Springer’s reviews in Computational Intelligence.
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An illustrative example of the crossover function overgeneralization problem. , α+ and α− ). Since the ranges for the threshold values depend on each individual application, the search space may be large. In this investigation an exhaustive search would be impractical. Thus, we propose to use a GA approach to find approximate optimal threshold values as follows. The HBA uses Equation (1) as the fitness function and the dataset T2 as a calibration dataset. The GA approach has been applied here because Equation (1) is not unimodal.
Notes: • Apply a GA approach on Steps 5 to 7 by using Equation (1) as the fitness function and T2 as a calibration dataset to find the classification model S1 and the optimal threshold values + − + − ( α * , α * , β * , β * ). • For the unclassifiable points by S1 in T2, we use Steps 3 to 7 with the optimal threshold values + − ( α * , α * , β * , β * ) to infer classification model S2. 10. Let S = S1 ∪ S2. Output: A new classification system S. + − the additional Fig. 1. The HBA • Sub-Problem #2: Break the inferred patterns into hyperspheres.
The initial population size is 20 (this size was determined empirically). The algorithm creates the crossover children by combining pairs of parents in the current population. At each coordinate of the child’s chromosome, the crossover function randomly selects the gene at the same coordinate from one of the two parents and assigns it to the child. In order to help motivate the crossover function, we consider the two chromosomes A and B depicted in Figure 6. Assume that the chromosomes A and B consist of the − + − + − + − four genes (α+ 1 ,α1 ,β1 ,β1 ) and (α2 ,α2 ,β2 ,β2 ), respectively.
Computer and Information Science by Fabio Perez Marzullo, Rodrigo Novo Porto (auth.), Roger Lee, Haeng-Kon Kim (eds.)