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Table 1 Comparison of core/periphery fitness measures using Beck et al. (2003; ND) data
2004
"... In PAGE 5: ....P Boyd, W.J. Fitzgerald, R.J. Beck/Social Networks columns 4 and 5 of Table1 . Column 6 of Table 1 compares the results from the UCINET (Version 6.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [ Table1 about here] From the results in Table 1, the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 5: ... For all 12 groups, all three of these algorithms matched the exhaustive search by consistently finding the global optimum from several starting configurations. [Table 1 about here] From the results in Table1 , the genetic algorithm in UCINET finds the global optimum in two out of our 12 cases. The UCINET fit statistic is among the five best for seven of the 12 cases, and among the ten best for nine of the 12 cases.... In PAGE 7: ... A low probability along with an intuitively high observed fitness value suggests that the observed data may have a core/periphery structure. To illustrate this permutation test, we used Mathematica to program a random permutation generator based upon the observed within group distribution of messages for each of the 12 groups from Table1 . As with the observed data, diagonal cells were also ignored for these permutations.... In PAGE 7: ... For Group 1, for example, no random permutation in each of the 3 runs produced an optimal fitness value equal to or greater than the observed fitness value of 0.867 (see Table1 ). For Group 3, 43 of the random permutations in the first run produced optimal fitness values equal to or greater than the observed fitness value (0.... ..."
Cited by 1
Table 2 Optimal neural network configurations found using genetic algorithm
in Combining Genetic Algorithms, Neural Networks and Wavelet Transforms for Analysis of Raman Spectra
"... In PAGE 8: ...8 5 5.2 0 50 100 150 200 Generations R M SE P ( % ) Data compressed to 16 data points Data compressed to 32 data points Figure 4 Fitness of best individual on island number 16 The configurations for the neural network with 16 and 32 inputs chosen by the genetic algorithm are detailed in Table2 . Table 2 Optimal neural network configurations found using genetic algorithm ... ..."
Table 1: Comparison of Artificial Immune Systems to Genetic Algorithms and Neural Networks.
2006
Cited by 10
Table 1: Comparison of Artificial Immune Systems to Genetic Algorithms and Neural Networks.
2006
Cited by 10
Table 2: Technology Mapping results
"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."
Table 1: Feature selection techniques based on evolutionary approach. Technique Researcher Genetic algorithm Zhang, P. et al. (2005);
"... In PAGE 2: ... Therefore to find the optimal features, some researchers use evolutionary approach such as genetic algorithm, particle swarm optimization, ant colony optimization, artificial fish swarm algorithm and others. Table1 gives some studies on feature selection techniques based on evolutionary approach. Table 1: Feature selection techniques based on evolutionary approach.... ..."
Table 4. It was shown that simulation evolution through the use of genetic algorithms could indeed be used to evolve a colony of robots with superior task performance, compared with initial colony.
1996
"... In PAGE 6: ...0000000000 101 000001 1 1001 110101 1001 11 101 0 1011110000 0100001110 0000100011 01 0001 1 100 0111000100 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 Post-Evolution 0000000000 11001 11010 000001 01 11 1110010001 0001 10101 1 1100000001 1011011110 1001100000 0001000100 0100000010 0001000010 0000000001 0000111000 01 001 00000 0000000000 0100100000 Table4 : Robot representations. REFERENCES Agah, A.... ..."
Cited by 8
Table 1 Radio network configuration.
"... In PAGE 2: ... While the macro- and microcells apply FH following the standard GSM algorithm, the picocells use a different FH algorithm instead, which is also a part of the GSM CTS specification [9] and was proposed in [10]. Table1 summa- rizes these assumptions. Each layer is modeled with an independent Poisson call arrival process.... ..."
Table 1 Genetic Algorithm Parameters
2005
"... In PAGE 12: ... 3.4 Other parameters Other parameters used by the GA runs are in Table1 . Single values in the table are shared for all networks having total variables N, while multiple values are those that di er for di erent N.... ..."
Table 1* Back Propagation Genetic Algorithm
1995
"... In PAGE 11: ... In each case network architecture is that of Figure 1. The parameters used in the training of the network are given in Table1 . Two measures of fit, namely the sum of squares deviation (Ssq.... In PAGE 17: ... Table1 . The parameter settings to run the neural network using both the back propagation and the genetic algorithms to run all three problems are given.... ..."
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