### Table 2: For the 25 problems in dimension n = 10, n = 30, and n = 50, number of function evaluations (min, 7th, median, 19th, maximum, mean and standard deviation) needed to reach the neighborhood of the global optimum with the objective function error value (accuracy) as given in the Tol column. A run is successful if it reaches Tol before n 104 function evaluations. For functions 13 to 25 none of the runs reach the given accuracy. Success rate (ps) and success performance SP1 and SP2 as de ned in Eq. 1 and Eq. 4. Standard deviation for SP2 as de ned in Eq. 6. .

"... In PAGE 4: ... The observed maximal nal population size is = 640; 448; 480, which means 26; 25; 25 times start = 10; 14; 15, for n = 10; 30; 50, respectively. According to the requirements, Table 1 reports CPU- time measurements, Table2 gives the number of function evaluations to reach the success criterion (if successful), the success rate, and the success performances as de ned in the previous section. The objective function error values after 103, 104, 105 and n 104 function evaluations are presented in Table 5, 6 and 7.... ..."

### Table 1. Description of the HIFF, IsoPeak, and IsoTorus fitness functions. The first column describes the objective funtion, the second the size of the individual, and the third and the fourth contain are the optimum solutions and their respective fitness values.

2004

"... In PAGE 11: ... We tried three standard optimization problems in the discrete domain such as HIFF, IsoPeak, and IsoTorus, which are known to be complex and full of local optima. Table1 de- scribes briefly these three functions. The reader can find more information on these problems in (Santana, 2004).... ..."

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### Table 17 Objective function values

"... In PAGE 23: ... The optimum cutting parameters found by the proposed volume sectioning and GA methodology as well as the catalogue values are tabulated in Table 16. The associated objective function values are given in Table17 . The possible cutting strategies with their total objective- function results are given in Table 18.... In PAGE 23: ... The possible cutting strategies with their total objective- function results are given in Table 18. As can be seen from Table17 and Table 18, in which the objective function values based on optimum machining parameters found by CPOS are given in comparison with those from handbook recommendations [20], considerable cost or time savings have been achieved with the optimal parameters in all cases. For the minimum production time, the best cutting strategy is found to be (1-1-1-1-1, i.... ..."

### Table 15: The Impact of the Population Sizes on the Performance It turns out that increasing the population sizes reduces the infeasibility ratio 18

1999

"... In PAGE 19: ...rom the optimum objective function value is 19.89%. The most important method parameters of the genetic algorithm are the sizes of the parent and the child population. Hence, Table15 gives some insight into what happens if these values are varied. All other parameters are kept as they... ..."

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### TABLE II Initial and Optimum Objective and Constraint Values.

2007

### TABLE VI Initial and Optimum Objective and Constraint Values.

2007

### Table 3. List of Test Functions

### Table 2: Local optima with corresponding objective function value #28f#29 for the deterministic cantilever beam

in Multipoint

2000

"... In PAGE 6: ... The optima have been determined by evaluation of each grid point in the space 1 6 x i 6 10, i =1;::: ;5. are given in Table2 . Three groups of local deterministic optima can be identi#0Ced, sharing the same objective function value.... In PAGE 7: ...0 Table 3: Calculated optimum solutions of the stochastic cantilever beam problem starting 200 optimization runs from #2810,10,10,10,10#29. A solution is categorized as a group-I pointof Table2 , a discrete neighbor of group I, or an other point. No.... ..."

### Table 1 Sizes of design parameters of the optimum design for CaPaMan, Fig 5.

2001

"... In PAGE 9: ... The constr optimization algorithm of the Matlab Optimization Toolbox, [17], has been used to solve optimal parameter values. Figure 4 shows the workspace of the optimum designed CaPaMan and Table1 gives the dimensions of the synthesized architecture. Figure 5 shows the evolution of the objective function of Eq.... ..."

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