### TABLE l Parameter values fitted by the optimization algorithms for the test problem*

1976

### Table 4.19: Comparison of the Pnc 2 with the Rise algorithm

### Table 7. Estimate Breaks in Variance by the ICSS, and Apply to the Absolute Stock Returns

1999

"... In PAGE 21: ... To avoid this problem, we use the ICSS method to identify breaks in variances of stock returns by using the model (19) - (21). Table7 reports the number of sudden changes in variance as identified by the ICSS algorithm for stock returns. Periods 3 and 7 have 17 break points and period 9 has only 4 change points and so on.... In PAGE 21: ... The significant changes in variance are a little bit more than those in level of absolute returns. The 3rd panel of Table7 shows the results of fitting breaks that correspond to the points of breaks in variance to the level of absolute stock returns. When breaks in variance are introduced, the evidence is somewhat mixed.... ..."

Cited by 19

### Table 8. Estimate Breaks in Variance by the ICSS, and Estimate d and LM statistics of the Squared Stock Return, Break Process and Squared Residuals

1999

"... In PAGE 22: ...negative estimates of d in the residuals are obtained, so there is some possibility of overdifference as pointed out in section 5. As an additional analysis, we also examined long memory in the squared stock returns in Table8 . As occasional breaks are incorporated directly into return series, the existence of long memory in volatility is mixed, too.... ..."

Cited by 19

### Table 1. Execution time, number of visited states and improvement wrt initial state

"... In PAGE 8: ... 4.3 Experimental results In order to validate our method, we implemented the proposed algorithms in C++ and experimented on the variation of measures like time (we present it in Table1 as the volume of visited states), volume of processed rows, improvement and quality of the proposed workflow. We have used a simple cost model taking into consideration only the number of processed rows based on simple formulae [17] and assigned selectivities for the involved activities.... In PAGE 9: ...consequently, for medium and large cases we compare (quality of solution) the best solution of HS and HS-Greedy to the best solution that ES has produced when it stopped (Table 2 and Figure 9). Table1 depicts the number of visited states for each algorithm, and the percentage of improvement for each algorithm compared with the cost of the initial state. We note that for small workflows, HS provides the optimal solution according to ES.... ..."

### Table 1. Execution time, number of visited states and improvement wrt initial state

"... In PAGE 8: ... 4.3 Experimental results In order to validate our method, we implemented the proposed algorithms in C++ and experimented on the variation of measures like time (we present it in Table1 as the volume of visited states), volume of processed rows, improvement and quality of the proposed workflow. We have used a simple cost model taking into consideration only the number of processed rows based on simple formulae [17] and assigned selectivities for the involved activities.... In PAGE 9: ...consequently, for medium and large cases we compare (quality of solution) the best solution of HS and HS-Greedy to the best solution that ES has produced when it stopped (Table 2 and Figure 9). Table1 depicts the number of visited states for each algorithm, and the percentage of improvement for each algorithm compared with the cost of the initial state. We note that for small workflows, HS provides the optimal solution according to ES.... ..."

### 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 2. Results of minimizing (1) with respect to n, r and k with 40 initial points.

### Table 1. Dependence of the average number of loops in the self-avoiding polymer on the number of monomers, and on the average number of branches. Data is from the cubic lattice, and the branching probability is fixed to be 0.1.

1979