### Table 5. Summary of results: average near- optimal capacity design for 5 station flow shop production community.

"... In PAGE 5: ... Run AA #12 Improvement#28#25#29 1 29; 314 4:8 2 32; 752 17:1 3 32; 226 15:2 4 22; 263 #2820:4#29 5 26; 149 #286:5#29 6 31; 985 14:4 7 28; 361 1:4 8 32; 527 16:3 9 32; 703 16:9 10 29; 822 6:6 Average 29; 810 6:6 shop designed by our two-agent community. Table5 shows the average capacity for 5 station flow shop designed by our two-agent community. One interesting finding is that the two-agent community can design a production line that bal- ances itself, as shown in Table 5.... In PAGE 5: ... In summary, the findings by this two-agent community are promising and encouraging. First, perhaps the most in- teresting finding for this study is that artificial agents are able to design an adaptive production line ( living factory ) that balances itself ( Table5 ). Second, in terms of optimal design and configuration, the difference between the results by the artificial agents approach and IPA is less then 10#25 (Table 2).... ..."

### Table 3: Throughput comparison Greedy Near-Optimal

2006

"... In PAGE 10: ... All results are the average over 20 random topologies generated by the setdest tool [21]. Table3 shows the maximum, minimum and average end- to-end throughput over 20 random topologies for both greedy and near-optimal ad-hoc relay. Greedy ad-hoc relay protocol achieves throughput gains of 572 897% with an average throughput gain of 785%.... ..."

Cited by 3

### Table 13: Improving Near-Optimal Tours with DPC

in Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study

### Table 13: Improving Near-Optimal Tours with DPC

in Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study

### Table 1: Near-optimal feature transform functions.

"... In PAGE 6: ... This leads us with the following scoring function: score(d) = n X i=1 wiTi(Fi(d)) For each feature, we chose a transform function that we em- pirically determined to be well-suited. Table1 shows the chosen transform functions. We tuned the scalar weights by selecting 5000 queries at random from the test set, us- ing an iterative refinement process to determine the weight that maximized the given performance measure, fixed the weight, and used the remaining 23,043 queries to assess the performance of the scoring function.... ..."

### Table 1: Near-optimal feature transform functions.

"... In PAGE 6: ... We chose transform functions that we empirically determined to be well-suited. Table1 shows the chosen transform functions. This type of linear combination is appropriate if we as- sume features to be independent with respect to relevance and an exponential model for link features, as discussed in [8].... ..."

### Table 2: A greedy heuristic to nd cost of near-optimal key sequence

### Table 2. Cycles to nd nearly-optimal solutions

1997

"... In PAGE 14: ... We also measured the number of cycles SBB consumes to outperform the nearly optimal distance. Table2 shows for each class the measured number of cycles for the nearly optimal distance with the real optimal distance in parentheses. We can see that IDB reaches the nearly optimal distance much sooner than does SBB for all classes.... ..."

Cited by 39

### Table 1: Optimal and near optimal solutions comparison Movement-Based

"... In PAGE 12: ... Sensors reliability is assumed to be fixed all the time. As shown in Table1 , the two heuristics are generally able to achieve reasonable average coverage performance. The coverage performance of movement-based greedy ... ..."