### Table 3. Density estimation techniques in multi-objective evolutionary algorithms and operators used in this study.

2001

"... In PAGE 11: ... Many advanced multi-objective evolutionary algorithms use some form of density dependent selection. Furthermore, nearly all techniques can be expressed in terms of density estimation, a classification is given in Table3 . We will make use of this as a further step towards a common framework of evolutionary multi-objective optimizers, and present the relevant enhancement of the unified model.... ..."

Cited by 20

### Table 3. Density estimation techniques in multi-objective evolutionary algorithms and operators used in this study.

2001

"... In PAGE 11: ... Many advanced multi-objective evolutionary algorithms use some form of density dependent selection. Furthermore, nearly all techniques can be expressed in terms of density estimation, a classification is given in Table3 . We will make use of this as a further step towards a common framework of evolutionary multi-objective optimizers, and present the relevant enhancement of the unified model.... ..."

Cited by 20

### Table 2 gives an overview of these different techniques and the implementations we use in our experiments. As a baseline, the D8D6D9D2CRCPD8CT BC operator is included, which represents the unlimited archive.

2001

"... In PAGE 9: ... Table2 . Archive truncation methods in multi-objective evolutionary algorithms and operator instances for this study.... ..."

Cited by 20

### Table 2 gives an overview of these different techniques and the implementations we use in our experiments. As a baseline, the D8D6D9D2CRCPD8CTBC operator is included, which represents the unlimited archive.

2001

"... In PAGE 9: ... Table2 . Archive truncation methods in multi-objective evolutionary algorithms and operator instances for this study.... ..."

Cited by 20

### Table 3. The average time for obtaining a solution for the multi-objective optimization problems by using TGP. The results are averaged over 30 independent runs.

"... In PAGE 13: ...igure 5. Diversity metric computed at every 10 generations. The results are averaged over 30 independent runs. Numerical values of the convergence and diversity metrics for the last generation are also given in section 9. 8 Running time Table3 is meant to show the efiectiveness and simplicity of the TGP algorithm by giving the time needed for solving these problems using a PIII Celeron computer at 850 MHz. Table 3 shows that TGP without archive is very fast.... In PAGE 13: ... 8 Running time Table 3 is meant to show the efiectiveness and simplicity of the TGP algorithm by giving the time needed for solving these problems using a PIII Celeron computer at 850 MHz. Table3 shows that TGP without archive is very fast. An average of 0.... ..."

### Table 1: Main features of selected evolutionary algorithms.

2002

"... In PAGE 4: ... Moreover, some EAs incorporate non-genetic features like adaptation and local search. Table1 summarizes the features of the EAs mainly re- viewed in this paper; note that they all rely on pure random initial populations. While the approaches SAWEA (Bcurrency1 ack et al.... ..."

Cited by 6

### Table 1: Iteration counts for iterative solutions of FIT2P. Algo- rithm switched phase at step 17.

"... In PAGE 22: ... The results for FIT2P are tabulated in Table 1. Table1 : Iteration counts for iterative solutions of FIT2P. Algo- rithm switched phase at step 17.... In PAGE 23: ...Indeed, as shown in Table1 , the number of PCG iterations taken to solve the normal equations generally increases as the IPM converges to a solution. On the other hand, when the two-phase algorithm switches to the RAE system (which occurs at the 17th IPM step), the number of SQMR iterations taken to solve the preconditioned RAE system generally decreases as the IPM solution converges.... ..."

### Table 1 Average time, needed to algorithms convergence

2006

"... In PAGE 4: ...et of connection weights. In Fig. 2 the comparative average results of four algorithms are presented. Table1 presents the average time in ms, needed to find the optimal solutions. The obtained results show that the proposed self-adaptive dynamic mutation enables rapid convergence to optimal solution.... In PAGE 5: ... The results showed that CEP, FEP and IFEP approaches have insignificant distinctions, which is conditioned by the similarity of Gaussian and Cauchy distributions, while proposed network-weight based mutation strategy (see Section II-D), which includes both phenotype and genotype information increases the rate of successful mutations, which leads to rapid convergence of algorithm and enables to find solutions with the high precision (Fig. 3, 4, Table1 ). This significant acceleration is achieved by two components, incorporated in described mutation approach: the self-adaptive control parameter network weight (Fig.... ..."

Cited by 1

### Table 1: Adaptive Modeling Technqiues for Extrapolation and Convergence

1994

"... In PAGE 7: ... A large angle, indicating almost linear motion, requires rst-order convergence, and a smaller angle, indicating curved motion, requires second-order convergence. Table1 summarizes how angle of embrace determines the adaptive tracking and convergence algorithms. Second-order convergence generates a smooth curve between the object apos;s previous absolute position, current displayed position, and the convergence point on the tracked path (Figure 6a).... ..."

Cited by 18

### Table 1: Adaptive Modeling Technqiues for Extrapolation and Convergence

1994

"... In PAGE 7: ... A large angle, indicating almost linear motion, requires rst-order convergence, and a smaller angle, indicating curved motion, requires second-order convergence. Table1 summarizes how angle of embrace determines the adaptive tracking and convergence algorithms. Second-order convergence generates a smooth curve between the object apos;s previous absolute position, current displayed position, and the convergence point on the tracked path (Figure 6a).... ..."

Cited by 18