Documentation of tbx3sga
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GEAOPT = tbx3sga
ToolBoX function to define parameters for the Simple Genetic Algorithm (SGA)
This function defines parameters for the Simple Genetic Algorithm (SGA).
This includes the internal use of a binary representation (the variables can
be real, integer or binary).
Recombination/Crossover uses one-point crossover and a crossover probability
For mutation binary mutation must be used (because of the internal binary
representation). The mutation probability (default is 1) is internally
divided by the number of (internal) variables, thus only one binary value
per individual is mutated. Change this value appropriate. The used internal
value is presented on the screen at the beginning of the optimization.
Just one panmictic population is used.
The generation gap of selection is set to 1, thus no generation gap. Implicitly
the recombination/crossover probability of 0.7 works as an generation gap or
as elitist selection.
When using this parameter function, please compare to the other (more powerful)
parameter setting functions (tbx3real, tbx3comp, ...).
This parameter setting function is mainly provided to show you how to define
even the first and most simple genetic algorithm using the GEATbx. Can be used
for comparison or whatever you want. But these parameters are not recommended
for productive use - in nearly every real-world case they are not good.
Syntax: GEAOPT = tbx3sga
no input parameters
GEAOPT - Structure with newly defined options
See also: geamain2, geaoptset, tbx3bin, tbx3real, tbx3comp
This document is part of version 3.8
GEATbx: Genetic and Evolutionary Algorithm Toolbox for use with Matlab -
The Genetic and Evolutionary Algorithm Toolbox is not public domain
© 1994-2006 Hartmut Pohlheim, All Rights Reserved,