### 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 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.... ..."

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### 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.... ..."

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### Table 1 Results of the transformation compared to the genetic algorithm Problem parameters Genetic algorithm

2001

"... In PAGE 3: ... Firstly, we have solved to optimality a series of test problems from literature with an exact SPG- solver similar to that of Duin (1993). Table1 gives the results for the test problem set used in Dror et al. (2000).... ..."

### 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: SNAP Die Fab Status

1995

"... In PAGE 2: ....p. = wave pipelined, F.P. = oating-point In most of our work wevalidated our techniques by realizing implementations. Table1 is a summary of our chip fabrication activities. 2 Algorithms and Systems In the remainder of this paper we summarize some of the techniques and algorithms whichhavebeende- veloped.... In PAGE 5: ... Ea lt; Eb - same as case (a) with Ea and Eb swapped. Table1 : SNAP Addition Rounding Operations. tive, the result itself is bit-inverted to obtain a possible mantissa.... ..."

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### Table 1: SNAP Die Fab Status

1995

"... In PAGE 2: ....p. = wave pipelined, F.P. = oating-point In most of our work we validated our techniques by realizing implementations. Table1 is a summary of our chip fabrication activities. 2 Algorithms and Systems In the remainder of this paper we summarize some of the techniques and algorithms which have been de- veloped.... In PAGE 5: ... Ea lt; Eb - same as case (a) with Ea and Eb swapped. Table1 : SNAP Addition Rounding Operations. tive, the result itself is bit-inverted to obtain a possible mantissa.... ..."

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### Table 2. Genetic algorithm (optimized power)/(initial power) for fixed compile time

2000

"... In PAGE 13: ... For the largest graph, the fixed simulation time was not long enough to make much improvement, but the best result occurred for , where the simulations are less frequent. Table2 summarizes the power reduction for the genetic algorithm with and without using the period graph, with a fixed compile time of one hour. 9 Conclusion This paper has explored a period graph model that enables efficient voltage scaling optimization for self-timed implementations of iterative applications.... ..."

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### Table 2. Genetic algorithm (optimized power)/(initial power) for fixed compile time

"... In PAGE 6: ... For the largest graph, the fixed simulation time was not long enough to make much improvement, but the best result occurred for , where the simulations are less fre- quent. Table2 summarizes the power reduction for the genetic algorithm with and without using the period graph, with a fixed compile time of one hour. 9 Conclusion This paper has explored a period graph model that enables efficient voltage scaling optimization for self-timed implementations of iterative applications.... ..."

### Table 2 Random search and parallel genetic algorithm comparison

1998

"... In PAGE 4: ... EXTENDED PARALLEL GENETIC ALGORITHM 2{ initialize population; create several equal evolution threads; wait while termination criterion is not reached; delete all threads; } Evolution thread forever{ perform tournament selection; delete selected individual; perform crossover; replace deleted individual; perform mutation; } Figure 5 The structure of parallel genetic algorithm with equal threads (EPGA_2) The parallel genetic algorithm was tested on several multidimensional problems. Table2 shows the results of the optimization of 38 dimensional approximation problem [14]. The global minimum of that problem is equal or greater than 0 (the smaller solution value is a better solution).... ..."

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