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Fig. 2-1: Output in Matlab command window at start of optimization run (used options)
Fig. 2-2: Status information displayed in command window during optimization (some lines removed)
Fig. 2-3: Graphical output during optimization
Fig. 2-6: Definition of objective function objexample1
Fig. 2-8: Definition of a larger number of variables and with extended boundaries
Fig. 2-9: Graphical output during optimization of first own objective function
Fig. 3-1: Definition of an objective function
Fig. 3-2: Definition of special return values of an objective function
Fig. 5-1: Layer model of the GEATbx
Fig. 5-2: Calling tree of the Genetic and Evolutionary Algorithm Toolbox (GEATbx)
Fig. 9-1. Procedure for solving optimization problems using evolutionary algorithms
Fig. 9-2. Structure of the system to be optimized as objective function
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
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 neighborhood: full and half ring
Fig. 3-6: Two-dimensional neighborhood; 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. 8-1: Classification of population models by range of selection (selection pool)
Fig. 8-2: Global population model (master-slave-structure)
Fig. 8-3: Local model (diffusion evolutionary algorithm)
Fig. 8-4: Unrestricted migration topology (Complete net topology)
Fig. 8-5: Scheme for migration of individuals between subpopulation
Fig. 8-6: Ring migration topology; left: distance 1, right: distance 1 and 2
Fig. 2-7: Visualization of Schwefel's function; surf plot in an area from -500 to 500
Fig. 2-9: Visualization of Sum of different power function; surf plot in an area from -1 to 1
Fig. 2-13: Visualization of Branins's rcos function; surf plot of the definition range
Fig. 2-15: Visualization of Goldstein-Price's function; surf plot of the definition range
GEATbx: | Main page Tutorial Algorithms M-functions Parameter/Options Example functions www.geatbx.com |