Fig. 1-1: Problem solution using evolutionary algorithms
Fig. 2-1: Structure of a single population evolutionary algorithm
Fig. 2-2: Structure of an extended multipopulation evolutionary algorithm
Fig. 3-1: Fitness assignment for linear and non-linear ranking
Fig. 3-2: Properties of linear ranking
Fig. 3-3: Roulette-wheel selection
Fig. 3-4: Stochastic universal sampling
Fig. 3-5: Linear neighbourhood: full and half ring
Fig. 3-6: Two-dimensional neighbourhood; left: full and half cross, right: full and half star
Fig. 3-7: Properties of truncation selection
Fig. 3-8: Properties of tournament selection
Fig. 3-9: Dependence of selection parameter on selection intensity
Fig. 3-10: Dependence of loss of diversity on selection intensity
Fig. 3-11: Dependence of selection variance on selection intensity
Fig. 4-1: Possible positions of the offspring after discrete recombination
Fig. 4-2: Area for variable value of offspring compared to parents in intermediate recombination
Fig. 4-3: Possible area of the offspring after intermediate recombination
Fig. 4-4: Possible positions of the offspring after line recombination
Fig. 4-6: Single-point crossover
Fig. 4-7: Multi-point crossover
Fig. 5-1: Effect of mutation of real variables in two dimensions
Fig. 6-1: Scheme for elitist insertion
Fig. 7-1: Classification of population models by range of selection (selection pool)
Fig. 7-2: Global population model (master-slave-structure)
Fig. 7-3: Unrestricted migration topology (Complete net topology)
Fig. 7-4: Scheme for migration of individuals between subpopulation
Fig. 7-5: Ring migration topology; left: distance 1, right: distance 1 and 2
Fig. 5-1: Layer model of the GEATbx
Fig. 5-1: Calling tree of Genetic and Evolutionary Algorithm Toolbox (GEATbx)
Fig. 1-2: Status information displayed in command window during optimization (some lines removed)
Fig. 1-3: Result information displayed in command window at the end of the optimization