 Fri Feb 22 17:09:28 2019, Suren Gourapura, Updates that need to be added to the manual
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We are redesigning the way we simulate antennas in our loop. To do this, we changed our simulationPEC macro skeletons and our output skeleton.
To make this easier, we changed the way we name the files, from i.uan where i is the simulation number, to i_j.uan where i is the antenna and
j is the frequency. |
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Thu May 11 15:57:08 2023, Ryan Debolt, Byran Reynolds, AREA Updates
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Here is some backlogged information as well as recent updates to our progress on AREA and its optimizations:
4/13/2023
We concluded our initial test of the AREA optimization loop. While analyzing our results, we noticed that most of our runs never reached our |
Thu May 25 15:36:54 2023, Ryan Debolt, Byran Reynolds, preliminary error tests (PUEO) 8x
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Here are some preliminary results from testing the effect of error on growth in the GA. For this test, we start with a simulated error of 0.5 because
our true fitness score is bounded between 0.0 and 1.0. From here, we simulate doubling the number of neutrinos by reducing the error by root(2), then root(4),
and observed the growth on the fitness scores plots. We did these three tests with both 50 and 100 individuals (plots starting with 30 use 50 and starting |
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Fri Aug 21 15:19:10 2020, Ryan Debolt, Friday Updates
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Name
Update
Plans for next week
Alex
M
Alex P
Eliot
Leo
Evelyn
Ryan
This
week I completed work on a set of graphs that shows many instances of of a run type on a graph to show the spread that runs give. I am still working on |
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Mon Nov 23 18:02:40 2020, Ryan Debolt, Monday Updates
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Alex M
Kept working with Amy, Alex P, Julie, and Ben on the AraSim
fix. We fixed our issue from last week but have a new one in stage 2. It looks like the issue has to do with resetting the values for V_forfft right before
stage 2 (around line 963). Check here for the current version of Report.cc: /users/PAS0654/pattonalexo/EFieldProject/11_23_20
Ryan
Fixed |
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Fri Feb 4 16:50:09 2022, Ryan Debolt, Loop Run
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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, |
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Fri Feb 4 17:59:41 2022, Ryan Debolt, GA Updates
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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. |
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Fri Feb 11 16:09:24 2022, Ryan Debolt, Parents.csv
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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:
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Thu Feb 24 20:08:46 2022, Ryan Debolt, Parents
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Fri Apr 1 16:35:50 2022, Ryan Debolt, Population test.
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https://docs.google.com/spreadsheets/d/1vvcmjByKfcns0-tbAjtePB8ZVGsXAKxXCfbMc99weMI/edit?usp=sharing Here is the spreadsheet link for the population
test. |
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Fri May 20 17:17:32 2022, Ryan Debolt, Fitness Functions Test 8x
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Bellow lies plots testing different scores and comparing them using a chi^2 score.
The functions used are as follows
Gaussian: e^(-2) (Red) |
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Tue Jun 21 13:20:41 2022, Ryan Debolt, Multigenerational Narrative draft
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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 |
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Thu Jun 30 13:04:48 2022, Ryan Debolt, Multigenerational Narrative draft 2
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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 |
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Fri Jul 8 13:34:08 2022, Ryan Debolt, Multigenerational Narrative draft 2
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Fri Jul 8 13:35:19 2022, Ryan Debolt, Multigenerational Narrative draft 2
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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.
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Tue Aug 9 11:36:24 2022, Ryan Debolt, GA User guide (pdf)
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Tue Aug 9 15:28:50 2022, Ryan Debolt, How many individuals to use in the GA.
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One of our foundational questions tied to the optimization of the GA has been "How many individuals should we simulate". Up to now, our minds
were made up for us by the speed of arasim being great enough that the time cost of simulating individuals was great enough that the improvements made
from having more were not enough to justify the slowdown. However, with the upgrade to the faster, more recent version of arasim, I decided to re-examine |
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Mon Oct 24 17:44:51 2022, Ryan Debolt, Icemc inputs
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| Here is our current assumption of the antenna angles needed for the icemc inputs. |
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Thu Nov 10 10:42:20 2022, Ryan Debolt, Optimizations
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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) |
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Fri Jun 2 00:21:36 2023, Ryan Debolt, Error test results
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Attached is a plot containing bar graphs with error bars representing the average number of generations it took for the GA to achieve a chi-squared value
of 0.25 (roughly equated to a 0.8 out of a max 1.0 fitness score). Unlike the fitness scores used by the GA, these values do not have simulated error attached
to them and are therefore a better measure of how well the GA is optimizing. These results were obtained by running 10 tests in the test loop for each |