Below is an example of our Parents.csv file written by the GA. This file tracks the parents of the individuals of the current generation. The columns and their contained information are as follows:

Current Gen: 

The numbered individual of the current generation.

Parent 1:

The number of the first parent of this individual as read from the previous generation.

Parent 2:

The number of the first parent of this individual as read from the previous generation.

Operator:

The genetic operator that created the individual in that row. 

I fixed a bug in the loop, so we started another rank test run. Run details in the attached file.

The bug was searching for the generationDNA.csv file in the wrong place, meaning that it wasn't able to copy it to the run directory. That meant we didn't have a record of the generation data in the usual format. I don't think that this explains the flatness, since the the generationDNA.csv file was still created every generation correctly, so the GA knew where it was. But this test now corrects that problem and tests the usage of the rank selection method.

The following plots are ittereations if the test loop that add increasing improvements to the GA.

The first plot shows the GA's behavoir unaltered from our previous runs (80% roulette, 20% tournament elite selection on).

The second plots shows when we use 90% rank selection and 10% tournament, elite selection off.

Plot 3 shows when we add an offset to restrict the values of the fitness function to be more within the range of the main loop.

Plot 4 shows when we add a gaussean mutation function that is applied to crossover individuals (rate and gaussean width chosen by guess).

The following are papers I have looked at while modifing the GA (not nessisaraly recently).

https://pdfs.semanticscholar.org/5733/418cbf21dedc9e5c04351ded4a989f1ff67e.pd

https://www.sciencedirect.com/science/article/abs/pii/0165607493902157

https://www.scientific.net/AMM.340.727 

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.28.1400&rep=rep1&type=pdf

https://arxiv.org/pdf/2010.04340.pdf

https://d1wqtxts1xzle7.cloudfront.net/30694440/10.1.1.34.9722.pdf?1361979690=&response-content-disposition=inline%3B+filename%3DUsing_genetic_algorithms_with_asexual_tr.pdf&Expires=1612908683&Signature=X93Gsc47AS0xqWf1SPLjG~7sNkoXSOXfnq1GpZ2QaPrYw9x9mWwASStW2IWexo7QBzbkhzcE5tZ~CmQA1MHN-paiNFIx2ed8VNS3IhesMnotKM0mSgUZ37BCleHT9BgGkUUum8mTJBAzCUaECn6RYjm1CZpfwVPC9zwuA~DnXBST4pGlQdna22D--sHwXgX~3U3gDUSxqk8mLI0gtn~Xued3XqsTGuMUKwJ2D9UpD5yp42-3IrH6d5CZREjEfXY2geTopQ-uNkr3eOriDj0UZqSrDw5mczmod3kQrQncgd~G2Kyda4RlIs8VDzQs~BGgszHJhSDAuKDrXr8P--9tVg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.438.7389&rep=rep1&type=pdf

https://arxiv.org/pdf/2102.01211.pdf

• Run Type
  • Main Arasim Loop
• Run Date
  • 02/04/2022
• Run Name
  • 2022_02_04_Rank
• Why are we doing this run?
  • To test rank selection in main loop
• What is different about this run from the last?
  • Rank Slection is being used.
  • Parents.csv introduced.
  • Elite is being turned off.
• Symmetric, asymmetric, linear, nonlinear (what order):
  • Non-linnear asymetric
• Number of individuals (NPOP):
  • 50 individuals
• Number of neutrinos thrown in AraSim (NNT):
  • 300,000
• Operatiors used (% of each):
  • 06% Reproduction
  • 72% Crossver
  • 22% Immigration
  • 1% M_rate (unused)
  • 5% sigma (unused)
• Selection methods used (% of each):
  • 0% Elite
  • 0% Reproduction
  • 10% Tournament
  • 90% Rank
• Are we using the database?
  • No.

Directory: /fs/ess/PAS1960/BiconeEvolutionOSC/BiconeEvolution/current_antenna_evo_build/XF_Loop/Evolutionary_Loop/Run_Outputs/2022_02_04_Rank

For PAEA Algorithm:

Part I: Complete as soon as the run starts

Run details: Please answer all of the questions below!

• Run Type
  • Answer here whether or not it's AREA (Gain pattern evolution) or PAEA (Bicone evolution)
• Run Date
  • Add answer here
• Run Name
  • Add answer here
• Parameters evolved
  • Add answer here
• Why are we doing this run?
  • Add answer here about what we are testing
• What is different about this run from the last?
  • Add answer here with info on what we are doing differently from the last run
  • Example: We are testing a new operator, we are changing the percentages of each selection method used, etc.
• Symmetric, asymmetric, linear, nonlinear (what order):
  • Add answer here (say N/A if this is an AREA run)
• Number of individuals (NPOP):
  • Add answer here
• Number of neutrinos thrown in AraSim (NNT):
  • Add answer here
• Operatiors used (% of each):
  • Add answer here
• Selection methods used (% of each):
  • Add answer here
• Are we using the database?
  • Add answer here. This can be found in the main bash script within the variables section. (Answer N/A for AREA run)

Please upload the text file with all run details before closing this!

Part II: Complete as soon as the run ends

Results: Once this run completes, please upload the plot(s) to this post as an attachment, as well as a general explanation of the results. 

• Summary and comments on results
  • Add thoughts on what we should do next here!
  • Example: "It seems like we are seeing minimal evolution. We should take a step back and try to see why we don't see improvement. Once we trouble shoot, we can start a new run to investigate."
  • Example 2: "Run looks good! Our best individual is in generation #12. Attached are the CAD drawings of that individual."

**Upload all plots, run parameter text files (file that has run settings saved), CAD models of best indivduals, etc**

For AREA Algorithm:

Part I: Complete as soon as the run starts

Run details: Please answer all of the questions below!

• Run Type
  • Answer here whether or not it's AREA (Gain pattern evolution) or PAEA (Bicone evolution)
• Run Date
  • Add answer here
• Run Name
  • Add answer here
• Parameters evolved
  • Add answer here
• Why are we doing this run?
  • Add answer here about what we are testing
• What is different about this run from the last?
  • Add answer here with info on what we are doing differently from the last run
  • Example: We are testing a new operator, we are changing the percentages of each selection method used, etc.
• Single frequency run or run with broadband frequency dependence:
  • Add answer here
• Number of individuals (NPOP):
  • Add answer here
• Number of neutrinos thrown in AraSim (NNT):
  • Add answer here
• Operatiors used (% of each):
  • Add answer here
• Selection methods used (% of each):
  • Add answer here
• Any other things to note?
  • Add answer here

Please upload the text file with all run details before closing this!

Part II: Complete as soon as the run ends

Results: Once this run completes, please upload the plot(s) to this post as an attachment, as well as a general explanation of the results. 

• Summary and comments on results
  • Add thoughts on what we should do next here!
  • Example: "It seems like we are seeing minimal evolution. We should take a step back and try to see why we don't see improvement. Once we trouble shoot, we can start a new run to investigate."
  • Example 2: "Run looks good! Our best individual is in generation #12. Attached are the CAD drawings of that individual."

**Upload all plots, run parameter text files (file that has run settings saved), gain patterns of the two best and two worst individuals, etc**

This is a plot made from the AREA project with full Arasim implementation with each gain pattern of each individual being fixed across all frequencies.

This run was done with 50 total individuals per generation, across 36 generations. Each individual was tested with 4 seeds of 10,000 neutrinos, for a total of 40,000 neutrinos. For each new generation, 25 individuals were created with roulette crossover, 8 with roulette mutation, 9 with tournament crossover, and 8 with tournament mutation.

individual 32 in gen 20 and individual 35 in gen 27 look promising (they have Veff > 8)

This is a plot made from the AREA project with full Arasim implementation with each gain pattern of each individual being fixed across all frequencies.

This run was done with 50 total individuals per generation, across 36 generations. Each individual was tested with 4 seeds of 10,000 neutrinos, for a total of 40,000 neutrinos. For each new generation, 25 individuals were created with roulette crossover, 8 with roulette mutation, 9 with tournament crossover, and 8 with tournament mutation.

This is a plot made from the AREA project with full Arasim implementation. It can be seen that the Veff of any individuals is not what I would consider "good", nor is it really rising, it is quite flat. This is because in this version of AREA, the gain pattern at each frequency is generated differently than each other frequency, there is no correlation. This is known and actively being corrected. This plot is of old data and was just made for two reasons: to make sure that the violin plotting script works for AREA, to display this early form of AREA that has been adapted for full Arasim.

This run was done with 50 total individuals per generation, across 36 generations. Each individual was tested with 4 seeds of 10,000 neutrinos, for a total of 40,000 neutrinos. For each new generation, 25 individuals were created with roulette crossover, 8 with roulette mutation, 9 with tournament crossover, and 8 with tournament mutation.

This plot is further detailed in Julie Rolla's doctorate thesis.

We presented at a workshop put on by the MODE collaboration. MODE is a collaboration dedicated to applying Automatic Differentiation to detector design. Here's the website: https://mode-collaboration.github.io/#:~:text=MODE%20(for%20Machine%2Dlearning%20Optimized,in%20space%2C%20and%20in%20nuclear

This ELOG post contains plots I made this week for comparing the antennas as they were evolved in the run being discussed in the upcoming paper with those same antennas when using realized gain instead of gain. These plots are preliminary, in that they should be edited before being placed in a paper (for example, VSWR is not in dBi).

Plots:

• Gain vs Realized gain
  • Polar plot showing the gain and realized gain of the best individual from the run in the paper
• VSWR/S11
  • The VSWR of the best individual in the run over the frequency bandwidth
• Gain differences
  • The difference between the gain and the realized gain for the best individual over the frequency bandwidth

Here are plots I made for the meeting on 9/3/21. These plots represent a comparison of the gain and realized gain for the 23rd generation of the run being discussed in the upcoming paper. Here is a list of the plots

• Gain vs realized gain
  • Polar plots of the best individual from generation 23 
• S11/VSWR plots
  • Shows the S11/VSWR over the bandwidth for the best individual

We began a new run using the same parameters as the previous one (see the last ELOG entry). The previous one ran for 8 generations. The difference between this run and the last one is that we have added in the polarity fix for AraSim Brian and Jorge gave us (copying the correct Report.cc and Report.h files into our AraSim version). Here are the run parameters:

Run Paramaters:
50 individuals
80/20 Roulette/Tournament
6% Reproduction, 72% Crossover, 22% Mutation
Run for 10 generations, then implement Jorge and Brian's fix for polarization in AraSim for the next run

We are currently running the loop using the improved parameters. The parameters are as follows:

50 individuals
80/20 Roulette/Tournament
6% Reproduction, 72% Crossover, 22% Mutation

Also note that individual 5 of generation 1 had its fitness score altered because one of its AraSim jobs didn't finish in time (and we would have had to wait 5 hours for it to rerun). To remedy this, I replaced the effective volume for the job with the average of the effective volumes from the other jobs for individual 5 (essentially meaning that it ran with 270k neutrinos instead of 300k). There is a .txt file in the directory titled Run_Notes.txt containing this message (and which will contain additional messages on similar things should they arise).

Run directory: /fs/project/PAS0654/BiconeEvolutionOSC/BiconeEvolution/current_antenna_evo_build/XF_Loop/Evolutionary_Loop/Run_Outputs/Improved_Parameter_Run_2021_03_09

Because of issues we were having with XF when running at the command line, we've shifted to running through a Pitzer VDI (see here: https://ondemand.osc.edu/pun/sys/dashboard/batch_connect/sys/bc_desktop/vdi/session_contexts/new).

The VDI will give you a mirror of a desktop running on OSC's side, so things like XF will run much faster. You can open a terminal by clicking the black box at the bottom of the screen and then run the loop like normal (./Asym_XF_Loop.sh). This has the added benefit of making it possible to exit out of your browser, close your laptop, or turn off your computer without interrupting the run, since the process is running on OSC's side rather than piping to your computer.

Here's the video I made a few months ago demonstrating how to run the loop. There might be some changes that have been made since then, but they'll generally be minor so it should still be representative of what running the loop looks like. It might need to be shared with you through the Microsoft drive service OSU gives us in order to view it, so either message me on Slack or email me at machtay.1@osu.edu and I'll share the link through there.

https://drive.google.com/file/d/1yL_eH_w2hXNt7J7YOwS5xl3ZHwGyl5JR/view

OSC managed to figure out our issue with running XF from an interactive job on Slurm. Previously, we were losing our connection to x11 forwarding. The solution is to use sinteractive instead of srun to obtain an interactive job. Here's the syntax:

sinteractive -A PAS0654 -t <time> -N 1 -n 8 -p serial

The -p serial flag denotes the type of partition to request. It's important to specify this, as otherwise it will default to the debug node, which has a limit of 1 hour.

We're going to begin a new run. The title is Machtay_2021_1_15_NPOP50_Asym . It is using the latest version of the GA Ryan has been working on (Latest_GA_Asym.cpp). We're using 6% reproduciton, 72% crossover, and 22% mutation. We are using 80% roulette selection. This correlates to the three numbers passed into the GA being 3 36 8 (since we're using 50 individuals).