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MMGAs> Crowding: Multi-Niche Crowding (MNC)

                                                                                                                                                                                                                                                                

 

Last update: 06/2011

 

 

     Parameters

 

 

■ Groups for selection (CS)

■ Groups for replacement (CF)

Size of CF (CG)

 

 

     Description

 

Multi-niche crowding method (MNC) modifies the selection system as well as the substitution one. This method introduces a selective pressure by having tournaments among groups of individuals to select the parents. Instead of using the parents in the replacement, various groups of solutions of the initial population are used. The individuals with the most similarities to the child in each group will be selected, and from these, the individual with the worst quality will be substituted.

 

In the MNC method the following process of selection takes place: an individual is selected randomly (Ii) and its pair (Ij) will be the closest to the CS individuals (crowding selection group size) selected randomly from the population with replacement. Thus, the recombination happens among individuals of the same niche, but not totally restricting the possibility to explore among niches.

 

During the replacement the policy followed is the worst among most similar. Hence, from the initial population, CF groups are made of CG size with random replacements. From each group the individual with the most similarities to the child which has just been obtained is achieved, and from these individuals the worst is selected and replaced by the child.

 

This process is shown in the following flowchart:

 

This method cannot be considered of type steady state as each child introduced in the population after the substitution is not available for the selection, but only for the substitution. I.e., from the initial population we obtain N parents necessary for reproduction (with only one step) by means of tournament. Once this is performed, each individual of the children population goes through the substitution process. The first child substitutes an individual from the initial population. For the second child the process is repeated but counting on the first child that is already inside the population from which the groups for substitution are formed.. So for child j prior children j-1 will be in the population.

 

 
 

     Executable

 

The following three files have to be in the same folder. Do not change the name of the file. Double-click the .exe file to run it.

 

 
  JSSP

Executable

exe

Data File

Open .txt file of the instance you want to run (for JSSP), copy and paste its contents inside this file.

D_Entrada

Parameters File

Open the file and change the parameters

D_Algoritmo

 

The following five files of solutions are generated:

D_Sal_Mejores-txt (includes the best solutions).

D_Sal_Optimos (includes the value of the objective function, i.e., Cmax).

D_Sal_NumOptimos (includes the number of different solutions where the optima value specified in the data file has been reached). This file is empty if this value is not reached.

D_Sal_Distancias (includes the distances between solutions where the optima value specified in the data file has been reached). This file is empty if either this value is not reached or only one solution is achieved.

D_Sal_Soluciones (includes the schedule of the solutions where the optima value specified in the data file has been reached). This file is empty if this value is not reached.

 

 

     References

 
Cede�o, W. and Vemuri, V. R. (1999). Analysis of speciation and nichng in the multi-niche crowding GA. Theoretical Computers Science (229) 177-197 Elsevier  

 

   

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