GEATbx: |
Main page Tutorial Algorithms M-functions Parameter/Options Example functions www.geatbx.com |

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

Parameter Options

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

Objective Functions

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 |

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