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Data Analysis and Future Plans in Optimization Simulations: Meeting Summary

This document presents data from previous simulations and ongoing work on 20-D and 30-D Rastrigin functions, including genetic plots, fitness averages, and best individual fitness. Future work includes researching uniform reproduction effects, stochastic mutation analysis, ancestral perspectives, and GeneSigma analysis in gene optimization. Experiments will compare the impact of GeneSigma values on large, medium, and small scales, alongside exploring timescales for constant values.

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Data Analysis and Future Plans in Optimization Simulations: Meeting Summary

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  1. Nov 21 Meeting John Nicholson

  2. 20-D Rastrigin – Gene Plots • This is data from previous simulations

  3. 20-D Rastrigin – SOP810 – Gene Sigma Mean

  4. 20-D Rastrigin – SOP810 – Gene Sigma Std Dev

  5. 20-D Rastrigin – 1SE810 – Gene Sigma Mean

  6. 20-D Rastrigin – 1SE810 – Gene Sigma Std Dev

  7. 20-D Rastrigin – 4SE30 – Gene Sigma Mean

  8. 20-D Rastrigin – 4SE30 – Gene Sigma Std Dev

  9. 30-D Rastrigin – Fitness Plots • This is data from not-yet-complete simulations • Still running: • 4SE30,3U • 1SE810,3N • 4SE30,3N • 3U is 3 children per individual, uniformly selected • 3N is 3 children per individual, two-phases of tournament selection

  10. 30-D Rastrigin – Population Average Fitness

  11. 30-D Rastrigin – Best Individual Fitness

  12. Future Work • Start writing conference paper(s). • Uniform reproduction…this time run simulations without uniform reproduction, and see how results change. • Stochastic analysis of multiple mutations • More problems • Ancestral views • Using pseudo-children fitness metrics to select • GeneSigma analysis • No GeneTau, keep GeneSigma constant, compare large/medium/small • GeneSigma with SelectionEventViewer • Timescales for GeneSigma constant values

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