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MMGAs> Clearing: Clearing

                                                                                                                                                                                                                                                               

 

Last update: 06/2011

 

 

     Parameters

 

 

Niche radius (σshare)

Number of winners (k)

 

 

     Description

 

Clearing method is based on the concept of limited resources to eliminate rivalry among different solutions. In nature life available resources for each species are limited and different from other species. This permits biodiversity as rivalry between individuals of different species decreases allowing cohabitation in the same geographical area (it is possible coexistence and survival of so different animals such as the lion and the ant as they do not need to fight for the same resources).

 

Following these principles, the clearing limits the selective pressure by modifying the landscape of the population before making a selection. The original P(t) population (with size N) is modified to form another P�(t) population (with size N�) for reproduction.

 

Clearing and selection: The process of forming the population P�(t) starts by putting in order from higher to lower quality, the solutions of the original population P(t). The first solution is selected and goes directly to the population P�(t). This first solution is called dominant as there is no better solution inside the population. Afterwards, each solution of the original P(t) population  is compared  with the selected solution, and the solutions which belong to a same niche are obtained (those solutions whose distance to the selected one is shorter than a radius (σshare)). The k best solutions called winners (those with higher quality) are saved  whereas the rest are assigned a null quality. This way, the competition inside the niche is limited leaving k solutions as survivors and the rest are not given any hope to survive in the selection process.

 

From this moment the process becomes iterative selecting the following solution of the ordering whose quality is different from zero. The iterative process finishes when all solutions pending to be selected in P(t) have zero quality. Reproduction is performed over the solutions of P�(t).

 

Elitism: The solutions from the P�(t)  population with a quality higher than the average of the original quality in the population before the clearing process, are selected to survive and pass directly to the following generation. The rest of positions are filled with the best children obtained.

 

This process is shown in the following flowchart:

 
 

     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 RCMPSP

Executable

exe

 Not available yet

Data File

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

D_Entrada

Not available yet

Parameters File

Open the file and change the parameters

D_Algoritmo

Not available yet

 

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

 
P�trowski, A. (1996). A clearing procedure as a niching method for genetic algorithms. In Proc. IEEE International Conference on Evolutionary Computation. Japan,798-803.  

P�trowski, A. (1997). A new selection operator dedicated to speciatin. In Proc. of the 7th international Conference on Genetic Algorithms. B�ck, T. (Ed.). Kaumann. San Mateo. USA., 144-151.

 

   

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