Documentation of tbx3steadyga

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Function Synopsis

GEAOPT = tbx3steadyga

Help text

 ToolBoX function to define parameters for the Steady State Genetic Algorithm (SSGA)

 This function defines parameters for the Steady State Genetic Algorithm (SSGA).
 The special feature of the staedy state GA is the small number of offspring per 
 generation. A large part of the population survives to the next generation unchanged.
 Often only 2 or 4 individuals are produced each generation. 
 Here 2 individuals are produced (GenerationGap is 2% of 100 individuals).
 But even a generation gap of 10-20% is still a small number.

 Because of the small number of new offspring each generation the number of
 generations must be proportionally larger. Here 10000 generations are used
 as termination criteria.
 Recombination/Crossover uses discrete recombination and a recombination/crossover 
 probability of 1.0.
 For mutation real mutation is used. The mutation probability (default is 1) 
 is internally divided by the number of (internal) variables, thus only one value 
 per individual is mutated. (The used internal value is presented on the screen 
 at the beginning of the optimization.)
 Just one panmictic population is used.

 This parameter setting function is provided to show you how to define 
 very well known genetic algorithm using the GEATbx.

 You can combine the power of the steady state GA with the power of multiple 
 populations (just define multiple subpopulation, here 4 subpopulations with 
 25 individuals each), the use of different strategies (just define different 
 parameters for the subpopulations, here different mutation range) and competing 
 subpopulations (just set Migration and Competition to on, load of parameters 
 from tbx3comp). However, when using 2 new offspring each generation, competition
 does not make sence (as each subpopulation still produces only 2 offspring each
 generation). Thus, competition is not switched on. 
 In this case the optimization must end after 2500 (10000 generations divided 
 by 4 subpopulation, as each subpopulation produces 2 individuals each 
 generation for a total of 8 individuals compared to 2 individuals in the 
 panmictic population case above).
 See the end of the function for the complete parameter definition. 

 As this "extended" EA is more powerful than the "standard" steady state GA 
 I leave the "extended" parameters settings switched on by default. If you 
 want to switch them off, just set MultiPop = 0 in the source code.

 Syntax:  GEAOPT = tbx3steadyga

 Input parameter:
    no input parameters

 Output parameter:
    GEAOPT    - Structure with newly defined options

 See also: geamain2, geaoptset, tbx3bin, tbx3real, tbx3comp

Cross-Reference Information

This function calls
GEATbx: Main page  Tutorial  Algorithms  M-functions  Parameter/Options  Example functions 

This document is part of version 3.8 of the 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, (