Table 10.1:

Overview of algorithm parameters

Table 10.2:

Experimental results achieved using a standard classical GA

Table 10.3:

Experimental results achieved using a GA with offspring selection

Table 10.4:

Parameter values used in the test runs of the SASEGASA algorithms with single crossover operators as well as with a combination of the operators

Table 10.5:

Results showing the scaling properties of SASEGASA with one crossover operator (OX), with and without mutation

Table 10.6:

Results showing the scaling properties of SASEGASA with one crossover operator (ERX), with and without mutation

Table 10.7:

Results showing the scaling properties of SASEGASA with one crossover operator (MPX), with and without mutation

Table 10.8:

Results showing the scaling properties of SASEGASA with a combination of crossover operators (OX, ERX, MPX), with and without mutation

Table 10.9:

Parameter values used in the test runs of a island model GA with variousoperators and various numbers of demes

Table 10.10:

Results showing the scaling properties of an island GA with one crossover operator (OX)using roulette-wheel selection, with and without mutation

Table 10.11:

Results showing the scaling properties of an Island GA with one crossover operator (ERX) using roulette-wheel selection, with and without mutation

Table 10.12:

Results showing the scaling properties of an Island GA with one crossover operator (MPX) using roulette-wheel selection, with and without mutation