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Entry time:	Tue Feb 3 18:01:28 2026
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<p>Here is some backlogged information as well as recent updates to our progress on AREA and its optimizations:</p> <p><strong>4/13/2023</strong></p> <p>We concluded our initial test of the AREA optimization loop. While analyzing our results, we noticed that most of our runs never reached our benchmarks for performance (chi-squared scores of .25 and .1) and that those that did contain abnormally high amounts of mutation. From previous runs using the GA for the GENETIS loop, we find that mutation and immigration (Note: AREA does not use immigration) usually play a smaller role in overall growth and are simply there to promote diversity. Crossover on the other hand is what handles the bulk of the growth done by the GA. Given our results ran counter to that, we decided to look further into the matter.</p> <p>Upon looking at the fitness score files, we could see that mutation-created individuals did not perform well in the best run types, but the crossover ones did contain the bulk of good scores. This lines up with expectations. However, closer inspection revealed that these crossover-derived individuals had extremely similar fitness scores, which could indicate elitism in selection methods leading to a lack of diversity in solutions. We then looked at a run that used only crossover with one selection type (roulette), and found every individual to have identical (and poor) scores that were the same up to around the 4th decimal place. This is a strong case that the selection methods used are too elitist to grow a population properly. </p> <p>We dove into the AREA GA to take a look at the functions doing the selection methods, and discovered several issues potentially causing excess elitism in the GA. It seems that the roulette selection does not behave the way we have used in our other GAs. There appears to be no weighting of individuals; rather, a threshold fitness score is collected and only individuals with a fitness score above the threshold are selected. This causes elitism, as only the most fit individuals are likely to pass this threshold. Tournament was found to be similarly troubled, with a bracket size of ⅓ of the population. This is again very elitist, as it is very large, making it very likely that only the best individuals will be selected. Before we try to further optimize the AREA GA, these selection methods should be addressed and fixed.</p> <p>For roulette, we propose implementing a similar weighting scheme for individuals as is used in the PAEA GAs that gives preference to the most fit individuals, but still allows others to be selected to encourage diversity of solutions.</p> <p>For crossover, we propose decreasing the bracket size. This would allow a more diverse range of individuals to be selected through “winning” smaller brackets that would have a lower probability of including top performing individuals.</p> <p><strong>4/20/23</strong></p> <p>We (Bryan and Ryan) met to work on improving the selection method functions in the AREA algorithm. </p> <p>For roulette selection, we added weighting by fitness score, as is done in the other GENETIS GAs, in an effort to prevent it from disproportionately selecting the most fit individuals only to become parents. As a first test, we ran an abbreviated version of the test-loop code, printing the fitness scores of the parents selected by the roulette method. The fix appears to have passed this initial test, as the fitness scores showed more variety and in the case of roulette crossover two unique parents seemed to be selected.</p> <p>For tournament selection, the only change necessary appeared to be decreasing the bracket size of the tournament(s), which in turn encourages other individuals besides the most fit to be selected as parents, therefore increasing diversity of solutions. We ran out of time to test this change fully, so we will pick up here in our next meeting.</p> <p>Looking further ahead, we will plan to work on refactoring the AREA algorithm to use the same functions as the other GENETIS GAs wherever possible. In the meantime, this working version can still be used for feature development so that progress is not halted.</p> <p> </p> <p><strong>5/11/2023</strong></p> <p>Upon looking at the results of the recent optimizations, our best runs are still taking over 40 generations to reach an optimized score. Given the speed of AraSim, this is still too slow to be considered optimal. Comparing this test loop run to the ones run on by the broader GENETIS GA, our best runs for the AREA GA are worse than the worst runs from those optimizations. As such, we have decided to pause optimizing the AREA GA in favor of adding it to the broader GENETIS one. As of today, we have finished a rough version of the generating function necessary to create new individuals. We have also started progress on the constraining functions and scaling functions that will be necessary to generate the spherical harmonics used to find the gain and phase. Once these functions are complete, we should be able to transplant this GA back into our test loop for AREA and resume optimizations.<br />  </p> Encoding: HTML ELCode plain Suppress Email notification

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