### 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: Design Data for Test Examples Genetic Algorithm Simulated Annealing Ex.

### 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 3: Partitioning Results: Comparison of our genetic algorithm with simulated annealing

"... In PAGE 8: ... The rst run has tighter architectural constraints compared to the second. Table3 presents the results of our comparison. For both the ga and sa the table shows (1) Best tness achieved for the given architec- tural constraints, (2) The least critical path delay obtained in the case of designs for which unit tness was possible.... In PAGE 8: ... (3) CPU run times. Table3 compares the performance of ga verses sa for 14 di erent tests. Each test was run several times with di erent random seeds for ga and sa and the average values are reported.... ..."

### Table 6 Benchmarking with a binary tree of 127 processes and a grid of 4 processors. It can be observed from tables 3, 4, 5 and 6 that the results obtained by simulated annealing are better than those of hill-climbing, but they are slower. The tables also indicate that a mapping comparable in quality can be obtained by simulated annealing and genetic algorithm, but genetic algorithm is less time consuming than simulated annealing, which illustrates the efficiency of the genetic search process. Figure 12 gives the evolution in time of the solution obtained by simulated annealing and genetic algorithms. They are both executed on a uniprocessor (T800 transputer). We show that for genetic algorithms the greatest reduction in the cost of the mapping occurs at the beginning. Thus a moderate quality mapping can be obtained very quickly.

1993

### Table 2 - Comparison of deGans results to simulated annealing

"... In PAGE 9: ...Table 2 - Comparison of deGans results to simulated annealing Table2 contains another set of set data and results. This data was constructed using the random timetable builder program, but the parameters were taken from a number of real school timetables produced in [1].... In PAGE 12: ... The parallel algorithm has been implemented on a conventional shared memory multiprocessor, a 10 processor Encore Multi-Max. Some of the test data from Table 1 (T1-1 through T1-8), and Table2 (number T2-13) was presented to the parallel program, and the... ..."

### Table 2. Methodology to infer parameters. SA = Simulated Annealing, GA = Genetic Algorithm, GD = Gradient Descent

### Table 1: Experimental parameter ranges for the genetic algorithm.

1996

"... In PAGE 9: ... per treatment. The parameter ranges used for each circuit are shown in Table1 for the genetic algorithm, and in Table 2 for the simulated annealing algorithm. For each graph, the mean cutsizes of the genetic algorithm and simulated annealing are compared.... ..."

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