Notes 3/25/15

*NO MEETING NEXT WEEK 4/1/2015*

*Hampshire Advising Day*

Lance

Mike

Bill

Lee

Nic

Wren

Karthik

Frode

Agenda:

Updates

Lance

Paper

Updates:

Lee-

Activity on GPTP papers

Submit something

Hampshire Students wrap up

Talk at Smith, post doc at Harvard

Nic-

GPTP, working with Thom

Helping Bill with Gecco, videos

Karthik-

GPTP 2014

Mike-

Midterms

Documentation

Produce; idea of style, where things go, accessible to newcomers

Wren-

Compare Bill’s regression to just diffusion in the tube

Lee says using curves as proxy

Bill says diffusion in a pipe equation

Lance made a possible equation

Frode-

Calc seems to be working

Doesn’t crash

Lee says next step is create github branch

Bill-

Wind turbine modeling paper

Identifying close loop dynamics of wind turbine…

Portugal

Tomorrow guest at UMass

Lance:

Programs as Polypeptides

 

3/11/2015 Lab Notes

Present: Tom, Nic, Bill, Wren, Frode, Eddie (Scribe)

No meeting next week for spring break!

Frode
– Calc problem is broken again
– Tests returns number, and boolean for error flag
– Currently error flag boolean logic is not being found by Push

Pucks
– Transfers between pucks depends on either the bids and asking matching, or a function comparing the two.
– There may, or may not, be loss currently in the transactions.
– How to determine if Pucks are showing emergent behavior?
– Perhaps the answer is with Mutual Information and Entropy
– Plundering
– – What determines the probability of success?
– – What should the cost of plundering be?
– – Can multiple pucks gang up on another puck?
– – – Split results of transactions between multiple parties.
– – How to decide if a puck should attempt a plunder?
– – A puck can only get back the amount of energy out of a plunder, is how much “damage” it managed to do to its victim.

Graphs/Visualization
– Lexicase has much higher diversity, and is more consistent.
– Lexicase has many more clusters.
– Bottom line: Lexicase promotes diversity and clustering.
– Visualization problems when runs are of different length.
– – Cannot turn x axis of graphs into percentage of run length, because drawing conclusions between successful and unsuccessful runs becomes problematic.
– Graphs can be seen at Tom’s Rpubs (with source code)
– – http://rpubs.com/thelmuth/clustering_runs2

Have a nice break!

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.