Author Archives: Mike Dean

Lab Notes 01.22.16

CI Lab Notes – 01-22-16CI Lab Notes


Present: Lee, Tom, Bill L, Bill T, Mike (Scribe), Nic, Eva, Eddie, George, Julian




You could win $$$!

Deadline in Spring

Very few entries in the past,

Probably not Auto Construction,

Mike Project idea


Fitness tests and birthing process are bottleneck

Check out Tozier’s Push interpreter

Andy tractor GUI  

Lee send this out to the group!

GUI to launch jobs.


Looking for mentors

Contact Eddie, Mike, or Lee if you’d like to be involved

CEC Niching Competition

Multimodal stuff.

Lexicase selection could be a good candidate for other

GECCO Paper    


                                Abstracts due Jan 27, Full Papers due Feb 3.

GECCO Poster    (Nic)


Poster about visualization

Tournament vs. Lexicase

Maybe turn it into a book?


Two solutions on the newest set of runs

One solved in 40 generations

2nd solved in 203 generations


Chris Hill  

Bill and Lee met with Chris

He works on ocean currents and turbulence. Large scale fluid dynamic simulations

From MIT, works with UMass as well.

Observation and Selection  

Foundational Questions institute


Quantum processing with QGAME

Evolve push programs that when run compile a QGAME script

Lack of complex numbers in Clojure is an issue

Does it need to be implemented in Clojure to make it run faster

It’s definitely running too slow at this point

Nic – Could Garbage collection be an issue? We should look into memory.


Lab Notes 03.25.15

Notes 3/25/15


*Hampshire Advising Day*















Activity on GPTP papers

Submit something

Hampshire Students wrap up

Talk at Smith, post doc at Harvard


GPTP, working with Thom

Helping Bill with Gecco, videos


GPTP 2014




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


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


Calc seems to be working

Doesn’t crash

Lee says next step is create github branch


Wind turbine modeling paper

Identifying close loop dynamics of wind turbine…


Tomorrow guest at UMass


Programs as Polypeptides


Lab Notes 3.4.15

Attendance: Lee, Tom, Nic, Bill, Wren, Beryl, Mike (Scribe), Karthik, Frode




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


Use lexicase selection with these clustering methods?


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.



Lee’s Note: *Consider effect of relative bid sizes for plundering.




Lab Notes 1/28/15

January 28, 2015













Gecco Papers -3

Thom, Bill, Nic



Asymmetry on parents

Collaboration with David Clark on math-finite terms and finite algebra, Clark has algorithms that are not evolutionary

Pucks; bonding, will then be able to do push based



Crossover revised paper; 2 problems, crossover bias and tournaments


Lee and Nic-

Measurement of success in systems, best approximation, best fitness

Both metrics are potentially useful, and when you don’t know…


Thom and Nic-

Generalization or how well matches the training set-over fitting?

No validation, but spot-check via graphs; stable, solid approximations over test area

Adding crossover bias-fitness becomes larger



Generalization and how to promote it in GP runs


Lee, Thom and Bill-

Papers from journal; collectively read and discuss

Trade of fitness and validation of fitness

How different system generalize-but not making better at generalization



Promoting generalization in symbolic regression

Smaller programs generally generalize better with better fitness

Random mate selection, survival step-fitness and fitness generality


Lee, Thom and Bill-

2 fitness cases,

Survival-how well fitness to 1, and how well fitness’s compare

2 sets similar-performance on one dependent on other

Size connects to post run simplification

Simplify with hill climber after

Each step turns some genes off (remove sub-expression)



Trees; mutations with replacement of sub tree with parent tree

Preserve syntax


Lee, Bill and Thom-

Post run simplification

Instead of pure hill climbing

Turn off two genes and turn one gene back on

Higher probability of turning things off then on

Frode tried to do systematic testing; same simplification but as genetic operator and how it works with size and generalization; all a wash, simplifying during a run has all sorts of repercussions

Post run simplification for generalization


Bill, Nic and Lee-

Epigenetic paper; danger of varying multiple things and their repercussions and relations within the system

What is the epigenetics contributing? Is it just turning off your other additions?

Tough sell without solving some unsolvable problems

Clarity about base system and what is being changed

Explanation of the nuances of ideas

Rational for starting point

2 stack based systems; epigenetics is easier to do in stack-based systems

What happens when stuff gets turned off?



Push for hill climbing; doesn’t take secondary

Keeps child if better at every test case, even with out lexicase



More LatB data

Calc concentration eq; relates to what the cell looks like

Models with Sarah



Documentation; example programs, how to put in your own data

Java version of push

Unwitting GP, Parasitic computing

Zeke’s tree based GP

Quill, Gorilla repl

Outsiders get started with out intensive tutorial

Cleaner; FourPush?




Trying inductive synthesis with Sketch (having trouble with strings)

Running Tom’s Benchmarks



Thinking about using simplification before parent selection.

Unclear whether this would help.



GECCO Papers



-In tree based GP, the root node is more important.
-Biological Reproduction is generally asymmetric


*GPTP wants “something” by 2/7.

*Gecco Workshop deadlines: 4/3

Notes – 1/7/15

CI Lab Meeting Notes – 1/7/15

Attendance: Lee, Tom, Karthik (Hangouts), Mike (Scribe)

     GECCO Papers: Abstracts due January 21st, Full papers on February 4th


               Benchmarks – Data is assembled, Tom is putting together the outline.

Estimate of 2 Weeks to finish runs.

               Parameter Sets – Instructions, Max Size, Push-eval limit, Max # of runs

List which parameters/instructions are being used for each problem.

               Homology – along with Bill?


How does simplification percentage affect generalization

Incorporate simplification into selection? (Probably a bad idea)

How does it apply outside of Push?


      Pucks bonding has been implemented


      Lee is working on re-implementing finite algebras algorithm



      Next week’s possible Agenda Items:

                Uncle Bob continued

Homology Discussion

More GECCO Submissions?

**Next Week’s Meeting will be on Tuesday, January 13th 10:00AM – 12:00PM Location: TBD


Computational Intelligence Laboratory Meeting Notes – 12/10/14


Present: Wren, Tom, Lance, Karthik, Lee, Nic, Bill, Tom, Tim, Mike (Scribe)


Announcements: Meeting next Wednesday 12/17/14 in ASH 221

Meetings next semester at same time in ASH 111 starting January 21st.


Agenda: Uncle Bob Discussion, Lab projects

Deadlines: GECCO Abstracts January 21st or 24th, Papers due February 4th


Project Updates:

Nic:          Working on clustering.

Bill:          Finishing up his paper on EHC, bald eagle project.

Tim:       Working on multivariate clustering of the BC data. Statistics is hard. “Use R” is a                                good book for learning R.

Post-run simplification and generalization with Tom?

Mike:     Compare GP to other methods of analysis. What does the solution look like?   Who                       would use it and why? For next week how interpretable results are? Look into credit                                  card neural networks.

Karthik:  Looking at implementing Tom’s benchmarks for GECCO in Sketch and another system.

Wren:     Working on setting up the Bill’s system. Bill is working on getting it running on FLY.

Tom:        “Pounding the Cluster”. EHC work, just added new data to the table. EHC is performing                                                 generally worse.

Lexicase paper proof is ready.

Lance:     Working on a chemistry representation for ALife. Recently completed a virtual                                                                  ribosome.

Uncle Bob (Finally) part one:

                Create an operator that generalizes within the genetic pipeline?


11 12 14 Notes

Meeting times Wednesday 9-12?. In ash 111.
Jason Moore now at Upenn.
Moshe and Lee grant submitted.
A ridiculously interesting and high volume amount of stuff and exciting graphs.
Uncle Bob.
Karthik giving tutorial.
Tom’s stuff.
Wren’s project. Wisconsin card projects.
evolving one model for two different conditions
individual is just the equation.
don’t evolve the parameter, but when testing an individual sweep the parameter.
Jordan Grafman Northwestern. Data.
TomWeed out the crap of simplifying programs. Mixed cross validation and simplification.
1. Niching
2. post run simplification
3. stats stuff that has been on the email list
4. stuff that started with hill-climbing. How do you limit runs?
5. multi-chance lexicase.
Replace space with newline program. Tournament selection just as a well as lexicase selection
if you look at it in a different problem.
Niching- Look at stuff
“I’m not sure what that means”.

8 October 2014

Attendance: Lee, Tom, Bill, Kwaku (yay!), Wren, Eddie, Noah, Tim, Karthik, Nic, Mike (scribe)


  • FLY was down due to power outages
  • Pucks – Shooters have been added, discussion of possibly using pixel data for proximity detection
  • Introductions for new people, individual project discussions.
    • Wren has data from her images of pollen tubes!
    • Tim implemented a decision tree in the Bladder Cancer dataset.
    • Bill has been using the UMASS computing cluster, can we use it?
  • Kwaku – Evolution and ecology in software engineering. Learning how diversity occurs. Embedded systems. May collaborate with Karthik?
  • Homology – How much of the population exhibits similarity structurally.
    • Background
      • Only takes structure and sequence into account, not function.
      • Comes out of ULTRA in hopes of finding structurally similar individuals
      • Plush replaced ULTRA
    • Only doing crossover on the same parts of a program
    • Want to measure how homology occurs/changes over time.
    • Tom has been testing out using edit distance to measure homology (Levenshtein) only using instructions.
    • Can we measure homology properly without executing the programs?
      • We want to measure fitness and homology, some changes benefits may not benefit until further generations
    • Should epigenetic markers influence homology? Probably.
      • Could look at the push programs rather than the plush genome.
      • Genomes are more important to look at.
      • Tom want to start without epigenetic markers for simplicity’s sake.
      • Can we use tags to assist us?
      • Padding is used to normalize.
    • Uncle Bob – SOSIES Paper code transformations, discuss next time.
    • Databases – To implement in Push, we need to figure out the root parent issue or an alternative representation to make Ancestry linear so we can find clades.
      • Find a way to eliminate duplicates?
      • Using ancestry change crossover parameters.

Link to Uncle Bob Blog Post