The following is a run summary of an AREA test using 100 individuals per generation, without the linear frequency dependence on the gain, with the frequency fixed (i.e. the same gain pattern is produced for all frequencies). This run was a preliminary test of the AREA algorithm with a larger NPop (100 individuals per generation) after seeing promising results in the previous test that only used 20 individuals per generation. The percentages of selection methods/operators used attempted to mimic the optimal percentages used for the PAEA Bicone Loop, but because the algorithms do not set these in the same manner, these are highly unlikely to be the best percentages to use. The results show a very quick plateau in fitness score with a loss in diversity of solutions after a low number of generations, potentially meaning that a poor breakdown of selection methods/operators was chosen. This result seems to underscore the need for a test loop-style optimization for AREA to determine the best breakdown of selection methods and operators to use for AREA.

Run details:

• Run Type
  • AREA
• Run Date
  • January 18, 2023
• Run Name
  • 20230118breynoldsrun_noLinearDependAREATest_38RCO_2RM_58TCO_2TM_150000NNU_2Seeds
• Why are we doing this run?
  • This test increased the number of individuals to 100 and attempted to mimic the percentages of selection methods and operators found to be optimal for the PAEA loop.
• What is different about this run from the last?
  • The previous test showed slow/minimal evolution after ~12 generations, and it was speculated that this was due to a small NPop of 20 individuals. This test uses an increased NPop of 100 individuals.
• Symmetric, asymmetric, linear, nonlinear (what order):
  • N/A
• Number of individuals (NPOP):
  • 100
• Number of neutrinos thrown in AraSim (NNT):
  • 300,000 Total (150,000 NNU x 2 Seeds)
• Operatiors used (% of each):
  • 96% Cross-Over, 4% Mutation
• Selection methods used (% of each):
  • 40% Roullette, 60% Tournament
• Are we using the database?
  • N/A

Results:

• Summary and comments on results
  • The fitness score quickly plaeaued and diversity of solutions was lost, potentially due to a unsuccessful choice of selection method and opterator percentages.
  • An important future step is to work with Ryan to create a test loop optimization for the AREA algorithm, as it is written differently in how it is passed selection method and operator percentages and requires its own optimization to find the optimal ratios of these. (Work on creating a test loop optimization for AREA is currently underway).

EDIT 4/10/23
Re-uploading gain pattern associated with most fit individual from this run