### Table 3. Simulation Results of Integrating Two Simple Heuristics

in Simple and Integrated Heuristic Algorithms for Scheduling Tasks with Time and Resource Constraints

1987

Cited by 21

### Table 4: Genetic Algorithm Performance on External Simulator

1999

"... In PAGE 34: ... PLACE TABLE 4 HERE We next looked at the 50 best solutions and 50 randomly selected so- lutions in the hybrid genetic algorithm population at 60, 70, 80 and 100 thousand evaluations using the external simulator. The results presented in Table4 suggest the solutions found at 60 thousand evaluations are basically as good as the solutions at 100 thousand evaluations. The best solution ever seen on this problem as evaluated by the external simulator is found in the randomly selected set after 70,000 evaluations: a mean time at dock of 378.... ..."

Cited by 10

### Table 4: Genetic Algorithm Performance on External Simulator

1999

"... In PAGE 34: ... PLACE TABLE 4 HERE We next looked at the 50 best solutions and 50 randomly selected so- lutions in the hybrid genetic algorithm population at 60, 70, 80 and 100 thousand evaluations using the external simulator. The results presented in Table4 suggest the solutions found at 60 thousand evaluations are basically as good as the solutions at 100 thousand evaluations. The best solution ever seen on this problem as evaluated by the external simulator is found in the randomly selected set after 70,000 evaluations: a mean time at dock of 378.... ..."

Cited by 10

### 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. The 50 pre-processing functions made available for evaluation by the genetic algorithm for the PLS optimisation study.

"... In PAGE 13: ... The RMSEP is given as: = = n i pred act n y y 1 2 / ) ( RMSEP , (3) where yact are the actual dependent variables, ypred are the predicted dependent variables and n is the number of objects. A GA was used to recover subsets of pre-processing functions from a total of 50 alternatives ( Table1 ). These are by no means a comprehensive list of spectral pre- processing algorithms available to the analyst; but represent the broad range of tools which in general can be split in to scaling, filtering, baseline correction and derivatisation categories.... In PAGE 15: ...5 x 106 days computation time on a 1.6Ghz PC! In Table 2 the best results from each of the independent runs are provided with the selected pre-processing functions encoded as detailed in Table1 . For comparison, details are shown for the model constructed against the raw data and for exhaustive searches using a single function.... ..."

### Table 4. Comparative Evaluation of Genetic Algorithms

"... In PAGE 10: ...3 0.3 - MinLT = Minimum lifetime (number of generations) 1 1 MaxLT = Maximum lifetime (number of generations) 7 7 - 4 Summary of Results Table4 presents a summarized comparison for each performance measure obtained with each algorithm. The results of each algorithm were averaged over 20 runs.... In PAGE 10: ... FAexGA#1 is the most effective configuration, since it found the highest number of distinct solutions in its final population. Table4 also shows that all genetic algorithms prove to be considerably more efficient for this problem than the conventional test generation methods. According to ... ..."

### Table 7: The F8F2 composition using an unweighted full matrix evaluation. This particular function has properties which may make it a poor test function for some genetic algorithms.

1996

Cited by 49