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Exploring Uniform Asexual Selection in Population Dynamics

This meeting outline delves into the concept of uniform asexual selection in population dynamics, touching on code implementation and its impact on reproduction rates. Understanding SOP status and exploring implications through data plots are emphasized. Future work includes conference paper writing, simulations variation, problem-solving, ancestral perspectives, and GeneSigma analysis.

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Exploring Uniform Asexual Selection in Population Dynamics

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Presentation Transcript

  1. Oct 18 Meeting John Nicholson

  2. Outline • Introduction/Status/Overview • Uniform Asexual Selection • What was going on with SOP

  3. Introduction/Status/Overview • What do I have in common with a politician? • We have been looking at “uniform” asexual selection recently. The old stuff from April was not uniform, but the new stuff from the last two/three months is uniform.

  4. Outline • Introduction/Status/Overview • Uniform Asexual Selection • What was going on with SOP

  5. Uniform Asexual Selection • I added this block of code some time over the summer: for (int i=0; i<m_PopulationSize; i++) { for (int j=0; j<numberOfChildrenToCreatePerIndividual; j++) { newIndividuals.add(deterministicallyChooseIndividual(i).reproduceAsexually()); numberOfChildrenCreatedSoFar++; } } • This block is part of the asexual reproduction routine, and uniformly spreads selection among the population as much as possible. • The variablenumberOfChildrenToCreatePerIndividual is the integer part of the asexual reproduction rate. • This block of code has now been parameterized.

  6. Outline • Introduction/Status/Overview • Uniform Asexual Selection • What was going on with SOP

  7. What was going on with SOP • The short version is that uniform asexual reproduction was going on, where every individual had a child. • I still have some pretty plots, so here they are:

  8. 5D-Rastrigin – Population Avg Fitness – 10 Runs

  9. 5D-Rastrigin – Best Individual – 10 Runs

  10. 7D-Rastrigin – Population Avg Fitness – 10 Runs

  11. 7D-Rastrigin – Best Individual – 10 Runs

  12. Future Work • Start writing conference paper(s). • Uniform reproduction…this time run simulations without uniform reproduction, and see how results change. • More problems • Ancestral views • GeneSigma analysis

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