### 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 5.4. Performance of Stochastic Search and Optimised Sequential Search Algorithms, (CPU time in seconds; and iterations of the stochastic search).

### TABLE I CLASSIFICATION RESULTS (IN %) FOR NAIVE BAYES,TAN, EM-CBL1 AND STOCHASTIC STRUCTURE SEARCH. XX-L

2004

Cited by 28

### TABLE I CLASSIFICATION RESULTS (IN %) FOR NAIVE BAYES,TAN, EM-CBL1 AND STOCHASTIC STRUCTURE SEARCH. XX-L

### Table 1. Classification of global optimization methods based on the degree of history dependence.

"... In PAGE 2: ... Finally, the small energy difference between the correct and incorrect minima and the exponential growth of the density of the non-native states with energy impose strict requirements on the accuracy of energy evaluation (less than about 1 kcal/mol)5. Numerous approaches have been used to attack the global optimization problem in protein structure prediction, with some success1-8 ( Table1 ). These methods are initially classified according to whether they are deterministic or not; stochastic methods are further subdivided according to the degree of similarity between conformations generated in consecutive iterations of the search algorithm.... In PAGE 3: ... Most of the MC-like stochastic global optimization strategies employ a three-step iteration: (i) modify the current conformation by means of a random move; (ii) evaluate its energy; (iii) accept or reject the new conformation according to an acceptance criterion. The random moves can be ranked by magnitude of change with respect to the current conformation ( Table1 ). The first group contains algorithms in which the generated conformations do not depend on the previous ones.... ..."

### Table 3: Properties of the frontier search

2000

"... In PAGE 18: ... To verify this result on a reliable bases monte-carlo simulation would be necessary. Table3 summarizes the properties of DEA, stochastic frontier and the fron- tier search algorithm introduced here. 6 Conclusion This paper introduced a genetic programming based methodology to determine production frontiers.... ..."

### Table 7: Results of ten independent runs of a stochastic optimization algorithm on the rst subproblem of Example 2

1998

"... In PAGE 15: ... local search phase. The results are given in Table7 . These are in accordance with the earlier reliable solutions, and they indicate that closely optimal result can be achieved by relatively simple tools too.... ..."

Cited by 3

### Table 6: Performance comparison of Binary and Gray coding in stochastic hillclimbing. L = 3.

2003

"... In PAGE 10: ... The tnesses used are as in the previous sections. Some representative results for L = 3 are shown in Table6 . As in the case of the genetic algorithm, fewer minima do not necessarily make the search easy for hillclimbing.... ..."

Cited by 1

### Table 5.1: Partial determinations found for each attribute of the voting domain. Search methods are hill-climbing (HC), stochastic search (SS), and depth- rst search to level three (DF3). Runtimes are in seconds, errors are percentages, and Gain is the gain in number of bits relative to the empty set.

### Table 2.- Schematic classification of stochastic optimization methods. Seminal references and selected examples of their application in process engineering are also given.

"... In PAGE 12: ...riori assumptions or pre-processing work. . There are at least four different classes of approaches which were apparently generated independently. A schematic classification, together with key seminal references and selected examples of their application in process engineering are given in Table2 . Some more details about each type follows: g183g32 Adaptive stochastic methods were developed in the domains of electrical and control engineering and applied mathematics (e.... In PAGE 13: ...stable configuration as slow cooling of a metal takes place). Apart from those methods presented in Table2 , during recent years a number of other (so called) meta-heuristics have been presented, mostly based on other biological or physical phenomena, and with combinatorial optimization as their original domain of application. Examples of these more recent methods are Taboo Search (TS), Ant Colony Optimization (ACO) (Dorigo, Maniezzo amp; Colorni, 1996; Bonabeau, Dorigo amp; Theraulaz, 2000; Jayaraman, Kulkarni amp; Shelokar, 2000) and particle swarm methods (Bonabeau, Dorigo amp; Theraulaz, 1999).... ..."