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vaeedaq

Variational AutoEncoder with Population Queue and AVS Copyright Sourodeep Bhattacharjee and Robin Gras, 2019.

To execute the code please enter the following from command prompt console or terminal: Python Main.py

The parameters of the algorithm are in Main.py and described as follows:

######################## PAREMETERS OF VAEEDA Q ######################## population = [] populationHistory = [] N = 1290 # Population Size n = 40 # Problem Size percentParent = 0.5 # Percentage of Parents to Keep in the next generation maxIter = 2000 # Maximum Number of EDA iteration to continue for tournamentSize = 2 # Size of Tournament tournamentProb = 0.8 # Probability of picking best in the tournament fitnessFunc = 1 # 0 - One Max #

headLess = 0 # Graphs are plotted when headless if off(0) , otherwise run in console supressConsole = 0 # Verbose detailed output is shown when set to 0 NKLanscapeTest=0 # Turn this to 1 to test on predefined NK Landscapes NKkvalue=6 # Value of K to use, N is taken from above N

######################## Fitness Function List ######################## 0 - One Max 1 - Trap 5 2 - Trap 7 3 - Trap 9 4 - Trap 11 5 - Trap 13

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vaeedaq's Issues

Assist.samplePopulationHistory does not exist

I ran your implementation of VAE-EDA-Q and it did not work.
One reason is that Assist.samplePopulationHistory does not exist, so selection from the population queue was not possible.
The second reason is that the individuals are not arranged in order of evaluation value because the array sorted by evaluation value is not assigned in Fittness.tournamentprob.

We corrected these, but the algorithm could not find the optimal solution.
I would appreciate you fixing these issues and re-uploading.

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