Asym Straight Sides (from the paper - Generation 23, Individual 8)

(inner radius, length, opening angle in radians)

2.08711,89.924,0.0161734
0.30175,45.3616,0.0910478

individual found in 

/fs/project/PAS0654/BiconeEvolutionOSC/BiconeEvolution/current_antenna_evo_build/XF_Loop/Evolutionary_Loop/Run_Outputs/AraSim_Polarity_Fix_2021_03_19/AraSim_Polarity_Fix_2021_03_19.xf/Simulations/001108/Run0001/

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. 

Currently, the PUEO loop runs pueoSim with 1 pueoSim process per job submitted.

Each of these jobs has 1 node and 8 cores, however, pueoSim only needs a single core to run.

Here is some data I collected by running the same seed of pueoSim with different numbers of cores in the job:

So, we should be able to run multiple processes of pueoSim without losing much performance.

Tests with more cores:

A test comparing the parallelization with the 40000 neutrinos per process we simulate in the loop:

So, these tests suggest we can simulate neutrinos ~20 times faster than currently done in the loop by utilizing more cores per job.

This method of multiple processes per job should be applicable to how we run other simulation software like AraSim as well.

(Note: both of these jobs had comparable queue times of 27 and 31 seconds respectively.

Also, I have tested and seen that the outputted root files are identical for the same seed regardless of if ran with multiple processes at the same time or with processes in serial)

(data found in /users/PAS1960/dylanwells1629/buildingPueoSim/testingouts/times.txt)

How to run multiple processes of pueoSim:

Previous Call:

./pueoBuilder/build/components/pueoSim/simulatePueo -i pueo.conf -o ${PSIMDIR}/outputs/${gen}_outputs/$SLURM_ARRAY_TASK_ID -r $run_num -n $NNT -e $Exp > $TMPDIR/out.txt

Multiple Processes Test Call:

for ((i=0; i<$threads; i++))

do

    ./simulatePueo -i pueo.conf -o $TMPDIR -r $i -n $NNU -e 19 > pueo.out &

done

wait

Instead of just calling peuoSim once per job, we can use the & symbol and a loop to run $threads number of pueoSim processes in the background and then wait for them to finish.

Standard Loop Architecture:

Complete an evolutionary step FOR EACH antenna before continuing on with the next step.

Steps:

1. Run the Genetic Algorithm for the entire population.

2. Run the XF radio simulation for the entire population.

3. Run the neutrino simulation software for the entire population.

4. Run root analysis and plots for the entire population.

However, due to constraints on the amount XF keys we have, we can only run 4-5 XF simulations at a time. 

So, when the first antennas finish their XF simulations, their outputs will simply sit there until EVERY other antenna finished their XF simulations.

New Proposed Architecture:

Complete necessary evolutionary steps as a population, but string together those that don't rely on data from other antennas.

Steps:

1. Run the Genetic Algorithm for the entire population

2. Run the XF radio simulation for each antenna

      - When an XF simulation finishes for an antenna, submit the pueoSim / root analysis jobs for that antenna

3. Run the plots for the entire population

This will allow us to complete most of our pueoSim computation while the XF portion of the loop is still running, cutting down the time between the final XF job finishing and the final pueoSim job finishing.

Test run of this new architecture with 100 antennas, 7,840,000 neutrinos per antenna.

After the final XF job finished, the pueoSim simulations and analysis of outputted root files were completed in 954 seconds,

Comparison Between the Two Architectures (both using job optimization from Elog 232)

Notes on Chart:

Source of data located in /users/PAS1960/dylanwells1629/buildingPueoSim/testingouts/times.txt and /fs/ess/PAS1960/HornEvolutionTestingOSC/GENETIS_PUEO/BiconeEvolution/current_antenna_evo_build/XF_Loop/Evolutionary_Loop/Run_Outputs/2023_07_24_test5/time.txt respectively)

Time from standard uses the time one of the 250 jobs running in parallel took in my testing of parallelizing processes inside of pueoSim jobs: 250 jobs * (40 * 40000 neutrinos per job) / 100 individuals = 4,000,000 neutrinos per individual)

The New Proposed time includes time spent analyzing the outputted root files to find fitness scores and errors, which would have taken around 100 seconds * population size for its number of neutrinos and files per individual (/fs/ess/PAS1960/HornEvolutionTestingOSC/GENETIS_PUEO/BiconeEvolution/current_antenna_evo_build/XF_Loop/Evolutionary_Loop/Run_Outputs/2023_07_23_test5/time.txt for data on this number)

So, this new architecture can provide more improvements for the amount and speed of neutrino simulation in the loop on top of the methods discussed in Elog 232.

This architecture could also be applied to see improvements in the Bicone and Hpol loops which are both affected by the limited number of XF keys.

Additional Notes:

1. For this new architecture test, each antenna uses 49 jobs for neutrino simulation instead of 2.5 previously. (49 pueoSim + 1 XF for 50 jobs per antenna, 5,000 jobs per generation)

2. The time for each antenna to submit, queue, and finish its neutrino simulation jobs must be less than the length of the XF job, or the extra time will accumulate for each antenna, losing much of the time benefits. (As long as it is less, the time spent on just pueoSim should be invariant under an increase in population)

3. The number of core hours spent on pueoSim jobs will be roughly the same for the same number of neutrinos as each job is shorter (except for a small contribution of the job overhead taking a higher percentage of total time for shorter jobs)

4. Initially I had thought that maybe queuing pueoSim jobs while running the XF jobs could slow down the queue for the XF jobs. So, I made the loop wait to submit the batch pueoSim jobs until we had space for all of them to be active with the ~250 max jobs per user.

Additionally, while observing my many tests, I didn't notice any correlation between the number of CPU peuoSim jobs in the queue and the number of GPU XF jobs out of the queue.

5. Branch I'm developing this on is here

6. The total time for the test generation was 15.4 hours, which is slightly longer than the ~14 hours from the 2023_05_08 run. However, this test also used double the population size, larger values for the range of antenna heights (on average about 3x taller), and 20x more neutrinos simulated per antenna. So, the actual speed is better than it first looks.

We began a new run for a symmetric bicone antenna. The purpose of this is to satisfy the most recent comments from the reviewer on the paper. We are using the same GA as for the paper with the same parameters. I will update this post soon with more details about this run. There is a run_details file attached, but please wait for me to update this post with more specifics as the version of the GA being used is less connected to the details presented in that file.

The data can be found here: /fs/ess/PAS1960/BiconeEvolutionOSC/BiconeEvolution/current_antenna_evo_build/XF_Loop/Evolutionary_Loop/Run_Outputs/2023_02_20_Symmetric_Run .

Plots we want to have for PUEO:

FScorePlot2D

Fitness_Scores_RG

VariablePlot (PUEO equivalent of LRT plot)

Veff_Plot (currently the veff is equivalent to the fitness score for PUEO)

Veffectives_RG

Rainbow_Plot 

Violin Plot

avg_freq

gain_vs_freq

polar_plotter

physics of results

Next steps:

Get the Error values out of the pueoSim root output (talking to Will)

Use output uan files from the new two-run XF simulation to test the frequency plots. (/fs/ess/PAS1960/HornEvolutionOSC/GENETIS_PUEO/BiconeEvolution/current_antenna_evo_build/XF_Loop/Evolutionary_Loop/Run_Outputs/2023_04_21_Test_6)

Implement the physics of results plots into the loop (Talk to Bryan / Dennis)

Get the average Veff/Fitness score from the default gain pattern in pueoSim to compare our results to.

Here are my recommended starting genes for the initial generation of the upcoming run (confirmed that these will produce a working antenna in XF). We want to use the lowest values we can because we expect that a larger (namely, taller) antenna will perform better:

S, H, x0, y0, yf, zf, beta
7.50000,50.0000,1.0000,1.00000,1.00000,50.0000,0.2

• S: Half the side length of the bottom of the antenna
• H: height of the antenna
• x0: distance of the bottom of the ridge from origin
• y0: width of ridge at the bottom
• yf: width of ridge at top
• zf: final height of the ridge (cannot exceed H)
• beta: a curvature parameter for the ridges

Slack: https://gpantennas.slack.com/messages 

elog:http://radiorm.physics.ohio-state.edu/elog/GENETIS/

Github link: https://github.com/mclowdus/BiconeEvolution

DropBox link: https://www.dropbox.com/home/GP_Antennas

Old GitHub for the evolution of the dipole: https://github.com/hchasan/XF_Scripts

This is the plot for the run type that had the best performance in the most recent optimization run that was completed. The most optimized runs from these collections are decided by which run types achieve a chi-squared value of 0.1 in the fewest generations (correlating to about a 0.9 fitness score). However, the genetic algorithm does not use Chi-squared directly, instead trying to maximize the fitness score (which uses a chi-squared in the denominator) that simulates the error we see in arasim. This fitness score is usually maximized at 1.0 but the simulated error can push the score slightly beyond this. This test was conducted with a simulated error of 0.1. This optimization holds the selection methods steady at 2 tournament 2 roulette and 6 rank selected individuals per 10 selection events. The changing parameter values are in the amounts of Reproduction, Crossover, Immigration, and mutation (rate and width) that the GA performs each generation. Each generation uses 100 individuals. In the graph below, the dotted lines are the minimum Chi^2 values at each generation, solid lines are the maximum fitness score. Each color represents a different run using the same parameters and dotted and solid lines sharing the same color represent the same run. The prevailing trend in the tests leading up to this test has been that the most optimized runs have low amounts of reproduction, high amounts of crossover, and they have been pushing the boundaries of the mutation rate and gaussian width of those mutations and this run follows that trend. The ranges of values we tested over were 0-12R, 88-100C, 15-25M_Rate, and 3-7 sigma, up from the previous test. This run took an average of 16.8 +/- 11.9 generations to reach the 0.1 chi-squared benchmark, the next closest run took 20.3 +/- 10.9 generations to reach the same point, while the most optimized run from an earlier test came in as the 10th best run taking 21.9 +/- 14.4 generations. The graph shows the behavior of this by showing large improvements in early generations that tend to quickly plateau around the maximum value before oscillating around the peak it reaches. Oscillations could be caused by either small mutations around our answer and displacement from the simulated error. The Chi-squared values show a much smoother behavior with smaller oscillations at the end of the run, which helps demonstrate the impact of the error on the behavior.

All data and plots discussed in this post are taken from this spreadsheet:

https://docs.google.com/spreadsheets/d/1BbpD81mugWQf10ozGDLI60takAn3tlvrq8ksT10yV5I/edit?usp=sharing

Run Details:

This optimization run was done to do a course search for the optimal set of parameters to run the GA with. This run used a fixed 100-individual population with a simulated error of 0.25 over 50 generations, to best replicate the current PUEO-loop environment. The selection methods have also been held fixed in this run. This run was tested using both the ARA and PUEO design to see if there were similarities in the best run type. This run encompassed 210 different run combinations that searched the following parameter space:

• 0-16 Reproduction, step sizes of 4
• 72-96 Crossover, step sizes of 4
• 4-16 Mutation, step sizes of 4
• 5-15 Sigma, step sizes of 5

Results:

Each run combination was run 10 times and we tracked the average number of generations it took for the distance metric to reach 0.05, which corresponds to a 0.95 true fitness score. The best runs for each design were as followed:

• ARA
  • Parameters
    • 12 Reproduction
    • 72 Crossover
    • 4 Mutation
    • 5 Sigma
  • Average gens to benchmark
    • 28.7 generations
  • Standard Deviation
    • 17.2 generations
• PUEO
  • Parameters
    • 0 Reproduction
    • 96 Crossover
    • 4 Mutation
    • 15 Sigma
  • Average gens to benchmark
    • 23.0 generations
  • Standard Deviation
    • 13.0 generations

We can see that these run types do not appear to have any strong correlations. In fact, looking at our complete data set, there does not appear to be a strong trend around any of the best runs. This issue became more prominent when we ran these best two run types for 100 tests rather than 10. When we did this, they returned averages of 44.4 generations for PUEO and 46.9 generations for ARA. For comparison, the average number of generations across all run types was 43.1 generations for PUEO and 42.8 generations for ARA. This seems to suggest that all of these run types are inherently inconsistent and regress to the mean given enough tests. Therefore, I do not believe we can draw any conclusions from this run.

Moving Forward:

Our ongoing hypothesis is that the size of the error could be causing inconsistency in how quickly our population can grow over time. Therefore, our next step is to repeat this experiment, but with zero error included, and see if we achieve results that show consistent behavior. If we can find more consistency in runs with no error, then we can more deeply explore the effects that error can have on GA growth. If there continues to be a lack of consistency, then we can look to other factors, such as population size, and try to find the root of the inconsistency.

Here are some scholarships available to physics majors I've been lucky enough to receive throughout undergrad that allowed me to work on this project unpaid without needing supplemental income from a separate job or loans. 

1. OSU Arts and Sciences Merit Scholarship Pooled Application 

• 500 word personal statement
• 1 letter of recommendation from a faculty member (Amy wrote mine)
• Varying amounts awarded (I got the $3,300 David and Velva Zarley Scholarship)
• Can be used for any education related expensed (tution, rent, food, ...)

2. James L. Smith Scholarship for Physics Majors

• 3 short essays (~200 words)
• Probably varying amounts (I got $4,000)
• Can be used for any education related expensed (tution, rent, food, ...)

3. Physics Class Awards 

• No application, chosen by department (just get good grades, honors might help. I didn't really talk to any of my professors or go to office hours, so milage might vary there)
• Freshman: $50, Sophomore: $250, Junior: $500, Senior: $?
• Can be used for any education related expensed (tution, rent, food, ...)

4. OSU Scholarship Universe

• A couple ~500 word essay
• Very few awarded, I've applied twice: $1,000 the first time and completely ghosted the second
• Can be used for any education related expensed (tution, rent, food, ...)

5. Licking County Foundation (If anyone is from licking county, I HIGHLY RECOMMEND APPLYNG TO THIS. I think there are similar ones for other counties) 

• ~500 word essay
• Varying awards (I've applied 3 times and gotten $2,375, $5,000, and $7,500) 
• Can be used for any education related expensed (tution, rent, food, ...)

Here is a link to a google drive with all my winning scholarship essays:

https://drive.google.com/drive/folders/1qJKpBf4by9wlReU5l_XjxjVsDIfXR4Jc

Attached is the license agreement that each person should sign to be able to use XF on OSC.  You can sign it, send it to Amy, and she will return it to OSC with her signature on it.

For each new student joining the group, we ask them to onboard by completing each of the following items in the order given:

(1) Go to osc.edu and request an account

• https://www.osc.edu/supercomputing/portals/client_portal/self_signup_for_accounts
• Fill out the info and for the Project Code put PAS1960 (and PAS0654 if you're able to do both).

(2) Complete the following tutorials and reading. Note that the order of these does not matter, however, I would suggest "how to use Git" comes last, as it is the lease important for you to start. Finally, I advise you work on a tutorial concurrently with reading the pages assigned below from my Dissertation. 

• OSC tutorials
  • Because this project uses a lot of computation power, we run on the Ohio Super Computing Center's clusters. Please utilize these tutorials to learn more. 
• Read Julie's Dissertation pages 1-9, 12-20 (starting at section 1.5.2), 28-37 (starting at section 2.3)
  • Feel free to read chapter 3, as it has the history of this project and goes into details of our most up-to-date endeavors. This is optional and not mandatory. 
• Complete the introduction to Bash project
• Complete the introduction to Python Project
• How to use Git (optional)
  • Git is a resource used for version control when writing software. See more for an introduction here: https://www.simplilearn.com/tutorials/git-tutorial/what-is-git
  • BEFORE reviewing this power point, I highly advise you take the code academy class https://www.codecademy.com/learn/learn-git. We use Git and GitHub to keep records and versions of the changes we make, so reverting back to other versions is doable. 
• Make sure to get an XFdtd license on OSC
  • See instructions here 

(3) Finally, review the full manual and complete the bullets below. The full manual is Appendix A of Julie's Dissertation.

• Appendix A of my dissertation is our manual and acts as the holy grail of our research. Anything you need can be found here (from where our info exists on the web, to how to run the code). 
• Once you have read this through entirely, we advise you log on to OSC and explore the code for PAEA in PAS1960. Once you have done so, complete this homework by making a copy of this google doc and filling it out on your own. 

Once these are completed, the students are ready to start helping. This is all of the coding information and basic background information on evolutionary algorithms students must know to be proficient in assisting. 

Other useful materials we have may include:

1. Our old GENETIS manual 
   1. This is somewhat out of date; however, it does have information on the general working on the loop. 
2. Julie's candidacy paper 
3. Complete the writing a genetic algorithm in C++ project
   1. This is still a work in progress! I'll complete it shortly!
4. Read chapter 3 of this book (more if you have time) by Eiben and Smith, "Introduction to Evolutionary Computation" for a quick introduction. The book should be available electronically from a university library.  Let us know if you can't find it there.
5. Get to know XFdtd
   1. XFdtd manual is also attached; however, the link above (in 5) is more useful for coding a .xmacro.
       

Important notes:

MAC USERS: Using a GUI when connecting to OSC via SSH from your terminal.

You will need to set up X11 forwarding. Otherwise, when you go to plot via SSH, they will not display (they don't forward from OSC to your machine). If you are unable to run GUI's on OSC through your computer's terminal, follow these instructions:

1. In your own system's terminal, type: sudo vi /etc/ssh/sshd_config
2. Search for the following line: X11Forwarding no
3. Change it to: X11 Forwarding yes. Save the file and exit.
4. In your own system's terminal, type: sudo systemctl restart sshd
5. For a Mac, download and install XQuartz.
6. Connect to OSC using ssh -XY username@hostname.
7. You can test that this works by typing 'xeye' or 'xclock' in the terminal when logged into OSC. 

Now you should be able to use OSC's GUIs.

PC USERS: 

As a PC user, there are a few things you need to do:

(1) Enable the Windows Subsystem for Linux on your device (https://developerinsider.co/stepwise-guide-to-enable-windows-10-subsystem-for-linux/#:~:text=To%20enable%20the%20Windows%20Subsystem,list%20here%20and%20click%20OK.)

(2) Get Bash on Ubuntu installed on your machine. You are going to need to have Bash on Ubuntu installed prior to starting these trainings; otherwise Bash commands will not be recognized.

•  https://ubuntu.com/tutorials/install-ubuntu-on-wsl2-on-windows-10#1-overview

(3) Get X11 forwarding working. Otherwise, when you go to plot via SSH, they will not display (they don't forward from OSC to your machine).

• Follow these steps here.  
  • http://courses.cms.caltech.edu/cs11/misc/xwindows.html
• Change your config file. To do so, in terminal (with Bash on Ubuntu open) 
  • type: sudo vi /etc/ssh/sshd_config
  • Then change '#X11Forwarding no' to '#X11Forwarding yes' and save it.
    • https://www.businessnewsdaily.com/11035-how-to-use-x11-forwarding.html
• Make sure Putty and Xming are running every time before you ssh in. Use Xlaunch application to run Xming and press yes to everyrthing.
  • Xming should now be running as a background task.
  • Open PuTTY and open the pre-saved like you saved in PuTTY. (This will open a Bash terminal that asks for your OSC login password.)

Here are the following things we need to work on:
 

1. Paper (Julie and Suren)
2. Get loop working-- specifically XF (Cade)
3. Test GA (Suren)
4. Gain pattern plots -- Make them 3D (Evelyn) & put on GitHub
5. Insert LR plot & Gain plot to loop (?)
   1. Edit bash script
   2. Update Github
6. AraSim plots (Julie and Max) -- see #2 and #3 here: http://radiorm.physics.ohio-state.edu/elog/GENETIS/7 for details. 
7. Update manual with things from Suren's last post (?)

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

This is a multigenerational tracing of our second-best individual's parents and children:

The second-best individual in this evolution was Bicone 22 from generation 40. This individual is, in fact, a fascinating case as we shall see. But to start the story of this individual we will go back to generation 38 in order to demonstrate some of the peculiarities. 

In generation 38 there were two bicones of no renown,  Bicone 11 and Bicone 17. Bicone 11 was a fairly average individual that was ranked 23rd with a fitness score of 4.72016. Its DNA was {*6.16084, ***79.663, 0.0015434, -0.107765} for side one , and {0.308809, 39.6742, -0.0084247, 0.40629} for side two. One day, by chance it met Bicone 17, another average bicone ranked 24th with a score of 4.71877 and DNA {**2.32499, 79.663, -0.00224948, 0.192602} for side one, and {0.308809, 39.6742, -0.0084247, 0.40629} for side two. These two individuals eventually became the parents of two antennas: Bicone 16 and Bicone 17 of generation 39.

Bicone 16 eventually grew to have been ranked 3rd in fitness score of 4.95323. It’s DNA ended up being a complete balance of its parents sharing side one with Bicone 38.11{2.32499, 79.663, -0.00224948, 0.192602} and side two with bicone 38.17 {0.308809, 39.6742, -0.0084247, 0.40629}. Bicone 16 was an individual with high aspirations and hoped to be reproduced. But alas, it was not meant to be. But bicone 16 came upon some amazing luck, it was selected with itself for crossover. This meant that bicone 16 was able to provide two identical surrogates to survive into the next generation. This is where this Bicone fulfilled its full potential. 

The twin bicones were named Bicone 22 and Bicone 23 in generation 40. Being clones, they shared all their DNA with Bicone 39.16. However, due to some circumstances, they had slightly different fitness scores. Bicone 23 managed a very respectable 5.0117 fitness score and was ranked 2nd in the generation. But not to be outdone Bicone 22 managed to score a 5.17014 and ended up being the second-best performing individual of all time. Being so successful, the two bicones ended up producing 8 children between the two groups.

Bicone 23 was the first to crossover and had 2 children with its partner. These were Bicones 4 and 5. Bicone 4 was ranked 39th with a fitness score of 4.60251 and still shared the DNA of its second side with its grandparent Bicone 38.17 as well as most of its first side with Bicone 38.11 {2.32499, 79.663, -0.00213879, 0.192602} {0.308809, 39.9608, -0.0084247, 0.40629}. Bicone 5 on the other hand, was ranked 14th with a fitness score of 4.80535 and it still shared a lot of DNA with its grandparents {6.16084,53.0851,-0.00224948,0.0534469} {0.966617,39.6742,-0.0084247,0.40629}.

Bicone 22 had 6 children of its own with various other Bicones. These were; Bicone 8, ranked 11th with a fitness Score of 4.81784 and DNA {2.32499, 75.9855, -0.00224948, 0.192602} {0.966617, 39.6742, -0.00320023, 0.213833}; Bicone 9, ranked 7th with a fitness score of 4.88966 and DNA {6.10508, 79.663, -0.000594616, 0.0351901} {0.308809, 42.4246, -0.0084247, 0.40629}; Bicone 12, ranked 12th with a fitness score of 4.81705 and DNA {6.42695, 75.9855, 0.0015434, -0.107765} {0.308809, 39.6742, -0.00320023, 0.213833}; Bicone 13, ranked 2nd with a fitness score of 5.0344 and DNA {2.32499, 79.663, -0.00224948, 0.192602} {0.966617, 42.4246, -0.0084247, 0.40629}; Bicone 34 ranked 3rd with a fitness score of 4.99864 and DNA {0.66148, 73.5522, -0.000594616, 0.00582814} {0.966617, 39.6742, -0.0084247, 0.40629}; and finally Bicone 35, ranked 22nd with fitness score 4.74955 and DNA {2.32499, 79.663, -0.00224948, 0.192602} {0.308809, 42.4246, -0.0084247, 0.40629}

Bellow, I have attached the rainbow plot with the parameters occupied by individual 4 in gen 41 which was again ranked 39th in that generation. From this, we can see that while in its own generation it was a poor performer, overall it was upper middle of the pack. However, because of the density of other better performing antennas in this region, it is hard to distinguish which genes in this antenna are contributing the most to the drop in fitness score compared to its siblings and parents. 


 

*Gene originating from Bicone 38.11

**Gene originating from Bicone 38.17

***Gene originating from Bicone 38.11 and 38.17 that is shared between the two.

The second-best individual in this evolution was Bicone 22 from generation 40. This individual is, in fact, a fascinating case as we shall see. But to start the story of this individual we will go back to generation 38 in order to demonstrate some of the peculiarities. 

In generation 38 there were two bicones of no renown,  Bicone 11 and Bicone 17. Bicone 11 was a fairly average individual that was ranked 23rd with a fitness score of 4.72016. Its DNA was {*6.16084, ***79.663, 0.0015434, -0.107765} for side one , and {0.308809, 39.6742, -0.0084247, 0.40629} for side two. One day, by chance it met Bicone 17, another average bicone ranked 24th with a score of 4.71877 and DNA {**2.32499, 79.663, -0.00224948, 0.192602} for side one, and {0.308809, 39.6742, -0.0084247, 0.40629} for side two. These two individuals eventually became the parents of two antennas: Bicone 16 and Bicone 17 of generation 39.

Bicone 16 eventually grew to have been ranked 3rd in fitness score of 4.95323. It’s DNA ended up being a complete balance of its parents sharing side one with Bicone 38.11{2.32499, 79.663, -0.00224948, 0.192602} and side two with bicone 38.17 {0.308809, 39.6742, -0.0084247, 0.40629}. Bicone 16 was an individual with high aspirations and hoped to be reproduced. But alas, it was not meant to be. But bicone 16 came upon some amazing luck, it was selected with itself for crossover. This meant that bicone 16 was able to provide two identical surrogates to survive into the next generation. This is where this Bicone fulfilled its full potential. 

The twin bicones were named Bicone 22 and Bicone 23 in generation 40. Being clones, they shared all their DNA with Bicone 39.16. However, due to some circumstances, they had slightly different fitness scores. Bicone 23 managed a very respectable 5.0117 fitness score and was ranked 2nd in the generation. But not to be outdone Bicone 22 managed to score a 5.17014 and ended up being the second-best performing individual of all time. Being so successful, the two bicones ended up producing 8 children between the two groups.

Bicone 23 was the first to crossover and had 2 children with its partner Bicone 25 (4.64409). These were Bicones 4 and 5. Bicone 4 was ranked 39th with a fitness score of 4.60251 and still shared the DNA of its second side with its grandparent Bicone 38.17 as well as most of its first side with Bicone 38.11 {2.32499, 79.663, -0.00213879, 0.192602} {0.308809, 39.9608, -0.0084247, 0.40629}. Bicone 5 on the other hand, was ranked 14th with a fitness score of 4.80535 and it still shared a lot of DNA with its grandparents {6.16084,53.0851,-0.00224948,0.0534469} {0.966617,39.6742,-0.0084247,0.40629}.

Bicone 22 had 6 children of its own with various other Bicones. These were; With Bicone 8.40 (4.82423) Bicone 8, ranked 11th with a fitness Score of 4.81784 and DNA {2.32499, 75.9855, -0.00224948, 0.192602} {0.966617, 39.6742, -0.00320023, 0.213833}; Bicone 9, ranked 7th with a fitness score of 4.88966 and DNA {6.10508, 79.663, -0.000594616, 0.0351901} {0.308809, 42.4246, -0.0084247, 0.40629}; With Biocone 16.40 (5.01137) Bicone 12, ranked 12th with a fitness score of 4.81705 and DNA {6.42695, 75.9855, 0.0015434, -0.107765} {0.308809, 39.6742, -0.00320023, 0.213833}; Bicone 13, ranked 2nd with a fitness score of 5.0344 and DNA {2.32499, 79.663, -0.00224948, 0.192602} {0.966617, 42.4246, -0.0084247, 0.40629}; With Bicone 4.40 (4.66578) Bicone 34 ranked 3rd with a fitness score of 4.99864 and DNA {0.66148, 73.5522, -0.000594616, 0.00582814} {0.966617, 39.6742, -0.0084247, 0.40629}; and finally Bicone 35, ranked 22nd with fitness score 4.74955 and DNA {2.32499, 79.663, -0.00224948, 0.192602} {0.308809, 42.4246, -0.0084247, 0.40629}

Upon inspection of the children in generation 41, you will see that none of the children surpassed their parent individual with the highest fitness score. They do however tend to have fitness scores around or above their second parent's score. Therefore, it is likely that the children's lower fitness score is due to a mismatch of genes. If we look at the rainbow plots provided, we can see that the genes of the worst-performing child are not dissimilar to those of individuals that scored higher than 5. This would imply that the fitness score found could be due to variations in Arasim similar to those seen from individuals 16.39 to 22/23.40 despite them being identical. Though it also could be possible that the graphs do not have a fine enough display to be able to clearly tell some of the genes apart. 

*Gene originating from Bicone 38.11

**Gene originating from Bicone 38.17

***Gene originating from Bicone 38.11 and 38.17 that is shared between the two.

Amy: We want to nail things down in places where we still have hypotheses for what could have happened.  For example, "This would imply that the fitness score found could be due to variations in Arasim similar to those seen from individuals 16.39 to 22/23.40 despite them being identical. Though it also could be possible that the graphs do not have a fine enough display to be able to clearly tell some of the genes apart."  These two hypotheses can easily be distinguished by running with higher statistics and looking at the numbers rather than relying on the resolution of the display.

Amy: Also, at each stage, I was looking for the random numbers that were generated that led to the selection of the fractions of the population that were used for each type of selection and operator.  

The story of individual 8 from generation 18. (draft)

Once, there was a curved bicone named individual 8 who was from generation 18. In many ways, it was similar to many other bicones but in fact, this individual was the best bicone that ever lived with a fitness score of 5.22097. It achieved this by having the following parameters; an inner radius of 6.31483, and 3.97024; lengths of  77.6017 and 40.9244; quadratic coefficients of, 0.00260615 and, -0.0103313; and finally, a linear coefficient of -0.197494 and

0.36119. Unfortunately, individual 8’s lineage ended with it as it never met that special someone (crossover) and didn't survive into the next generation (reproduction) despite having a 77.7% of doing at least one of these. 

This bicone had a sibling with a fitness Score of 4.65861 and had the following parameters: 6.05614,79.663,-0.000353038,0.0331969 (side 1)

1.92878,37.9849,-0.00213265,0.156724  (side 2).

Unfortunately, this bicone also suffered the same fate as its sibling and failed to leave a lasting mark on this hypothetical world. 

These two individuals had parents from generation 17 whose name’s were individuals 39 and individual 8. After having individuals 8 and 9 in generation 18, individuals 39 and 8 from generation had a nasty divorce (possibly leading to 8 and 9’s aversion to marriage) and remarried. In these marriages, they both produced two more children that would be our previous antenna’s step-siblings. 

Antenna 8 from gen 17 married antenna 46 to produce antenna 20 with a fitness score of 4.57122,  and antenna 21 with a fitness score of 4.71056. Individual 20 shared the same genes for the second set of linear and quadratic coefficients as its more successful step-sibling individual 8. 21 had no similarities to individual 8 but had 4 children in generation 19. 

Antenna 39 from gen 17 married antenna 5 to produce antenna 28 with a fitness score of 4.71808 and antenna 29 with a fitness score of 4.90119. Antenna 28 shared had an identical side 1 to individual 8 and also shared the same inner radius and length on the second side, and had 2 children in generation 19. Individual 29 had no similarities to individual 8 but really got around and had 8 children with various partners in generation 19.