### Table 2: Natural and artificial domains

### Table 1. Mapping between iNet artificial immune system and natural immune system

### Table 1 - Motivation theories for artificial systems and their parallels in nature.

"... In PAGE 13: ... 5. Discussion Table1 summarises the parallels between motivation theories for natural and artificial systems. It shows that a majority of motivation theories for artificial systems are based on drive theory, arousal theory and operant theory from natural systems.... ..."

### Table 5: mapping concepts in nature to evolutionary computing

2006

"... In PAGE 56: ...hich gives individuals, which are better adapted to their environment, i.e. have a high tness, a better chance to survive and produce new o spring. These concepts in nature are mapped to computers as shown in Table5 . The given problem, which we want to solve, is seen as an environment in which a population of individuals evolves.... ..."

### Table 4: Summary of group decisions (= token contributions to the public good) in the mechanism treatment with an interior equilibrium

### Table 1. Nature - to - computer mapping

in Kasapovic Scheduling Multiprocessor Tasks with Genetic Algorithms, - Applied InformaticsProceedings

2002

Cited by 1

### Table 2. Global distortion along the image sequence for the Artificial Neural Network in the

"... In PAGE 7: ... In table 1 we show the results of the Evolutionary Algorithms with several parameter settings: population size, number of offsprings allowed and frequency of interaction (number of generations computed between environment interactions). Table2 shows the results for the inherently adaptive algorithms: the Artificial Neural Networks and our Evolution Strategy. In the case of the Artificial Neural Networks we have applied them using the Euclidean distance and a penalized distance to compute the winner.... ..."

### TABLE 1 Summary of the Basic Features that Relate and Distinguish Different Types of Complex Networks, Both Natural and Artificial

2002

Cited by 7

### Table 1: Some simple linear models; Full Sample ( n = 34; t-statistics in parenthesis)

"... In PAGE 4: ... of .17. This relationship, moreover, does not disappear if we control for the variables usually considered in empirical studies of the relationship between election laws and party fragmentation -- the magnitude of legislative electoral districts ( D ), the presence or absence of adjustment districts ( AD ), and societal heterogeneity ( H ) -- or if we use the more common measure of party fragmentation, the `effective apos; number of parties, EN . 3 As Table1 shows, a directly elected 2 The data analysis presented in this paper was conducted using the SST package. In this and all subsequent regressions we computed heteroskedasticity-consistent standard errors using White =s (1980) method.... In PAGE 5: ...president has its own positive and highly statistically significant influence on the number of parties that compete for legislative seats. Of course, the number of observations in Table1 is not great ( n = 34). And since most countries in our sample are represented by more than one observation, one could suppose that the statistical pattern portrayed there can be influenced by the idiosyncratic characteristics of countries (i.... In PAGE 5: ... ), and endogenous institutional selection (i.e., an existing proto-party system that influences the choice of presidential versus parliamentary governmental forms). In fact, though, the coefficients for P in Table1 are relatively robust. For example, - If, to eliminate the country-specific effects allowed by multiple observations from each country, we take only one election per country (the second, because gt;founding = elections are considered suspect due to their plebiscite nature) we get N = 6.... In PAGE 14: ...lsewhere, in fact, we show that for established democracies (e.g., Canada, the United States, Germany, Finland, England, Australia, Netherlands, Iceland, and so on), this model yields a better fit than does one that merely incorporates H as a linear additive variable or that ignores H altogether (Ordeshook and Shvetsova 1994). But here we see that although the coefficients for H ln( D ) are statistically significant (except when our dependent variable concerns the number of parties winning seats), such a model for our complete sample ( Table1 ) is no better in terms of adjusted R 2 than one that ignores heterogeneity altogether, and is strictly worse when we restrict the analysis to Central Europe (Table 2). There is no indisputable explanation for this pattern, but the most evident hypothesis is that when democratic process is suddenly thrust upon a society, as it was in so many of the countries in our sample, parties designed to `serve apos; or otherwise take advantage of basic social cleavages coexist with the other parties that form at this time, and that total number greatly surpasses what can be sustained in equilibrium as a product of the combined influence of a polity apos;s electoral laws and social cleavages.... In PAGE 15: ... For another comparison with equivalent implications, consider Lijphart apos;s (1990) regime data, which also pertains to established democracies. Regressing `effective apos; number of parties against the log of average district magnitude yields the equation (see Table1 in Ordeshook and Shvetsova 1994) EN = 3.24 + .... In PAGE 16: ... Thus, as we subsequently argue, we cannot reject the hypothesis that the coefficient for D is zero in presidential systems. 12 These numbers come from the first regression in Table1 after setting P = 0 and D = 25, since 25 is the maximum district magnitude in our sample. 13 Notice the slight positive slope of the line for presidential systems since in this regression ... ..."

### TABLE 1 Summary of the Basic Features that Relate and Distinguish Different Types of Complex Networks, Both Natural and Artificial

2005