### Table 4: The Alternating Heuristic SBS

"... In PAGE 11: ...Table4 , in the worst case is equal to O(l2J); where l is the size of CON and J is the computational e ort to evaluate the classi cation quality of a given S: This algorithm alternatively uses the Best search algorithm and the First search algorithm at alternate levels. We have already analyzed these two algorithms.... ..."

### Table 2: Number of search alternatives in one col- umn

2007

"... In PAGE 3: ... One brute force approach to find the optimal solution is to perform a exhaustive search of all implementation alter- natives and estimate their power consumption. As demon- strated in Table2 , this is obviously impossible because of the huge number of possibilities. Therefore utilizing simpler methods and heuristics are inevitable.... ..."

Cited by 1

### Table 1: Automatic Programming using Stochastic Search

1995

"... In PAGE 9: ...e build- ing blocks, exists, genetic algorithms, even starting from a random collection of vectors, can progressively select the vectors with building blocks and using the crossover operator gradually splice these together until the population contains vectors which are substantially correct. Table1 lists the stochastic search techniques including GAs, used with the classes of pro- gramming languages to automatically generate programs. Clearly, work has been focused on using genetic algorithms, leaving the techniques relatively untouched.... ..."

Cited by 4

### Table 5. Search Measures for Heuristics

"... In PAGE 5: ...1 Data on search measures As the following table shows, when heuristics are compared with respect to measures associated with search, differences appear which clearly distinguish the two classes of heuristics identified by the factor analysis. In fact, for each of the mea- sures shown in Table5 there is no overlap in values for the different classes of heuristics. 5.... In PAGE 6: ... Table5 , there was no overlap in the averages for heuristics in the two classes. For contention heuristics, the mean level at which prom first equalled 1.... ..."

### Table 5. Search Measures for Heuristics

"... In PAGE 5: ... To this end, various measures were tested including mea- sures of overall search effort (here, search nodes), a mea- sure of the branching factor (mean CYCSCY for variables selected during search), a measure of connectivity with future, unin- stantiated variables (mean forward degree), and two failure measures defined in Section 2. The results of these tests are shown in Table5 , where data for contention heuristics are shown at the top of the ta- ble (rows 1-7), while data for simplification heuristics are shown at the bottom. In the first place, in these tests there are no consistent differences in overall performance (nodes) between heuristics of different classes.... In PAGE 6: ...) When this effect was examined for sets of 100 problems, it was found that promise reaches its maximum value sooner for simplification than for contention heuristics. As with the data in Table5 , there was no overlap in the averages for heuristics in the two classes.... ..."

### Table 2. Effect of heuristic search.

"... In PAGE 15: ... Localization queries have a very small accumulated running time, showing that pre-computation is crucial. In Table2 we depict the running time of the sweep-line algorithm as well as the effect of heuristic search, where tg is the time of the sweep-line algorithm, tc is the preparation time of the search algorithm (initializing the data structures) ts is the pure searching time for a single shortest path query, and #exp is the corresponding number of expansions done in computing the shortest path. As in the case of point localization the sweep-line intersection algorithm is more... In PAGE 16: ... Heuristic search can suc- cessfully be combined with geometric pruning. The smaller impact of heuristic search compared to Table2 can be attributed to the averaging effect of random queries, posing... ..."

### Table 2: Alternatives suggested by different heuristic criteria

"... In PAGE 11: ... The results obtained by the application of the different heuristics introduced in Section 4.2 are presented in Table2 , where each combination heuristic/technology specifies which is the solution, among those presented in Table 1, which was rated as the best for that technology... In PAGE 12: ...Prioritize the municipalities corresponding to the major producers 0 1 2 7 0 14 23 By combining the solutions suggested in Table2 , it is possible to reduce the set of solutions presented in Figure 2 to construct Pareto optimal set P for the problem to solve. In particular, the combination of the undominated solutions, from the viewpoint of the heuristic criteria used, spots out 11 solutions (number 1, 2, 3, 7, 10, 12, 13, 14, 16, 17, 23, respectively) which will be used for the construction of the set P.... ..."

### Table 2: tabu search heuristic

2005

"... In PAGE 16: ... 6.2 Results Table2 shows the results of both the initial and nal solutions of tabu search. The percentage deviation from the best (i.... In PAGE 16: ... This number provides us with the number of local minima encountered. As with Table2 , Table 3 also shows that results improve with an increasing value of t, but this is not the case for instances pr3a-pr3c. Solutions to instances pr3b and pr3c have the same number... ..."