Attendance: Lee, Tom, Nic, Bill, Wren, Beryl, Mike (Scribe), Karthik, Frode
Agenda:
Project Updates
GPTP Deadline
Paper Discussion
GPTP: Clustering, Post-run simplification for generalization,
Lee has funding (May 14 – 16 in Ann Arbor)
Project Updates:
Karthik – Working with search. Functions that can take an object as input and an object as output.
Beryl – Will be meeting with Herb later this week to discuss math.
Wren – Using fitted data to make a test point to see what that generates with Bill and his system
Bill – Wind cases. Original results were for 14 different wind cases, they are better models than what have been published. Getting results on just operational data. Having Issues with bloat
Nic – Markdown in R is super easy and makes pretty stuff. Creates a static HTML file.
Working on clustering with Tom. Trying to get Tom’s data into Neo4J.
Tom – Clustering
Frode – Fixed Calc. Changed the push args to fix it. Tom suggests having the data generated from data domains.
Looking at simplification.
Lee – Eddie is working with Pucks for A Life simulations. Working on implementing violence
(involuntary transactions). Stronger pucks can currently take from weaker pucks. Next, the ability to plunder should be probabilistic rather than absolute.
Time to come back to auto-construction. Variation in reproduction should evolve. Next some instructions in the program manipulate plush genomes. Anyone want to help?
Clustering – Kristoff Kravich Paper: Growing k gradually and using clustering from previous
result.
Use lexicase selection with these clustering methods?
Hypervolume?
Clustering is good. Let’s combine it with lexicase instead of Hypervolume.
Tom – Discovering how many clusters there are in every generation.
20 bits have to be different to be in different clusters.
150 clusters appear in 1000 cases.
Compared clustering vs diversity.
While clustering plateaus, diversity increases.
GPTP!
Lee’s Note: *Consider effect of relative bid sizes for plundering.