Group E hard at work finalizing our final paper for the course.
Pictured (from the left): Emily and Audra
Picture taker: Sam
|Figure 1. Abstract diagram of spatial resource heterogeneity. Each colored circle represents a patch in the world where a given resource is available at a high inflow rate. The panel on the left represents, where the patches barely overlap, represents high spatial heterogeneity. The panel on the right, where patches overlap more and up to four resources are available in a given place in the world, represents lower spatial heterogeneity.|
|Figure something. Spatial resource heterogeneity.|
|Figure something. Python script to randomly place resources in two patches in the world.|
|Figure. Entropy vs patch radius.|
|Figure something. How overlap effects entropy.|
|Figure something. Entropy treatments (in terms of patch radius size). Top phenotypic diversity, bottom, evolve equals by update 100,000.|
|Figure 1. Replication of Walker and Ofria (2012) results for phenotypic diversity across resource inflow rate.|
Panel A shows our replication, while panel B shows the original results. Similar to Walker and Ofria, we also found diversity to peak between inflow rates of 3-30. Our results were based on 30 replicate populations.
|Figure 2. Replication of Walker and Ofria (2012) results for number of populations to evolve EQU task across resource inflow rate. Panel A shows our replication, while panel B shows the original results. Similar to Walker and Ofria, we also found diversity to peak between inflow rates of 10-300. Our results were based on 30 replicate populations.|
|Figure 3. Sample code for environment configuration file. Note, for each reaction, the value increases with increasing task complexity (from 1 to 5). Also, the <type=pow> command as well as <requisite:max_count=1> command were specified to change how the task reward was implemented.|
|Figure 4. Python script used to extract, compile and plot data from data files.|