### Table 3: Hierarchical ATPG with different optimization techniques

"... In PAGE 13: ... We can also use the number of identified untestable faults to better estimate the fault coverage improvement in the proposed approach. The final results are reported in Table3 , and they detail the attained Fault Cover- age (FC%), and the required CPU time for each Optimization Algorithm. Behavioral test patterns are semi-automatically derived by extracting the CDFG of the design and then traversing it, in a negligible CPU time, in such a way to cover every branch.... ..."

### Table 1: Candidate Optimization Phases in the Genetic Algorithm along with their Desig- nationsOptimization Phase Gene Description

2003

"... In PAGE 44: ... If such a sequence is encountered then a log of that sequence is maintained so the developers can try to resolve the problem. Table1 describes each phase in the compiler and gives a designation (gene) of each phase that is used for displaying the sequence in the window in Figure 13. It may be the case that the performance desired by the user is achieved by an optimiza- tion sequence early on in the search process.... In PAGE 56: ... Similar to the results in Table 3, these sequences represent the optimization phases successfully applied as opposed to all optimization phases attempted. Some optimization phases listed in Table1 are rarely applied since they have already been applied once before register assignment. These are the control- ow transformations that include phases 1, 2, 3, 5, 6 and 7 listed in Table 1.... In PAGE 56: ... Some optimization phases listed in Table 1 are rarely applied since they have already been applied once before register assignment. These are the control- ow transformations that include phases 1, 2, 3, 5, 6 and 7 listed in Table1 . Strength reduction was not applied due to using dynamic instruction counts instead of taking the latencies of more expensive instructions, like integer multiplies, into account.... ..."

Cited by 1

### Table 1. Optimization parameters for genetic algorithm.

in Atmospheric Chemistry and Physics Optimizing CO2 observing networks in the presence of model error:

2004

### Table 1 Comparison of core/periphery fitness measures using Beck et al. (2003; ND) data

2004

"... In PAGE 5: ....P Boyd, W.J. Fitzgerald, R.J. Beck/Social Networks columns 4 and 5 of Table1 . Column 6 of Table 1 compares the results from the UCINET (Version 6.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [ Table1 about here] From the results in Table 1, the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [Table 1 about here] From the results in Table1 , the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 7: ... A low probability along with an intuitively high observed fitness value suggests that the observed data may have a core/periphery structure. To illustrate this permutation test, we used Mathematica to program a random permutation generator based upon the observed within group distribution of messages for each of the 12 groups from Table1 . As with the observed data, diagonal cells were also ignored for these permutations.... In PAGE 7: ... For Group 1, for example, no random permutation in each of the 3 runs produced an optimal fitness value equal to or greater than the observed fitness value of 0.867 (see Table1 ). For Group 3, 43 of the random permutations in the first run produced optimal fitness values equal to or greater than the observed fitness value (0.... ..."

Cited by 1

### Table 1. Primitives of the genetic algorithm

"... In PAGE 4: ...hifter and one subtractor (Fig. 4). Starting from these considerations, a digital filter can be described using a very small number of elementary operations. The primitives selected for digital filters are listed in Table1 . Each elementary operation is encoded by its own code (one character) and by two integer numbers, which represent the relative offset (calculated from the current position) of the two operands.... ..."

### Table 1. Comparison of results for various approaches.

"... In PAGE 8: ... 4. Numerical Results Table1 compares the balance and uniformity (t,s) of (n,2) de Bruijn sequences... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. ------------------------- Table1 goes here ------------------------- In Table 1, we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. -------------------------Table 1 goes here ------------------------- In Table1 , we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... ..."

### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."

### Table 2 Optimal neural network configurations found using genetic algorithm

in Combining Genetic Algorithms, Neural Networks and Wavelet Transforms for Analysis of Raman Spectra

"... In PAGE 8: ...8 5 5.2 0 50 100 150 200 Generations R M SE P ( % ) Data compressed to 16 data points Data compressed to 32 data points Figure 4 Fitness of best individual on island number 16 The configurations for the neural network with 16 and 32 inputs chosen by the genetic algorithm are detailed in Table2 . Table 2 Optimal neural network configurations found using genetic algorithm ... ..."

### Table 2: Simulated annealing amp; genetic algorithm--constrained optimization

"... In PAGE 4: ... The confidence intervals for 95% confidence level were calculated for both the algorithms. Table2... ..."

### Table 4: Genetic Algorithms community in the MAX ranking

"... In PAGE 5: ... The seed node for the MAX algorithm is the NCBI (National Center for Biotechnology Information) home page, which re- sults in a set of results that on the biological aspect of the query. Table4 shows the positions where the first 10 pages about ge- netic algorithms appear in the ranking of the MAX algorithm. The first page related to genetic algorithms appears in position... In PAGE 7: ... These two differences become obvious in our ex- perimental results. In Table4 , we observe that the pages about genetic algorithms are ranked higher in the ranking of MAX , and that there are some pages for which the CO-CITATION algo- rithm does not produce a meaningful ranking since the amount of co-citation with the seed node is zero. Similar phenomena can be observed for the abortion query.... ..."