Meeting: Feb 22

Lets stick to original project idea, changing landscapes.

Notes:

Don’t need to run for 1 million generations.

maybe 250,000?

MAIN ISSUES:

high mutation rates!!!

change form default- 0.025????

shorter organisms?-

makes it harder for them to evolve and makes it run faster.

Run 50 replicates each.

Maybe set up changes in events file instead of transferring organisms to new run- every X updates kill off Y percent of the population.

parameter ideas?

say Y = 95%

fast X = 500 updates

slow X - 5,000 updates

Lets make our population size be 10,000! 100 x 100 grid.

real Elena paper for more ideas re: mutation rate and pop size.

Maybe do a test run and look up average generation time to decide how many generations we want between die-offs?

try to tie in biology?

Meeting with Adviser

Mission "Schedule Meeting with Adviser" was a success!

We're on for Tuesday afternoon. Charles and Ian: is there anything else related to our last post you'd recommend talking to Art about?

Meeting Feb 16.

First meeting:
Feb 16, 3:00 pm

What about including more factors affecting evolvability in our project.
e.g. increased variation should increase evolvability.
Sexual reproduction increases variation- is this implemented in AVIDA currently?
Higher mutation rate.

Baldwin effect- e.g. learning? - not implemented in avida

Modularity- is there a way to implement modularity to make things more evolvable/ less likely to break under mutation?
What if we were to break down modularity by removing ability to have loops at all!
Maybe by modifying the instruction set and taking out jumps?

What about pre-adaptation or exaptation.
What if we evolved populations to be good at only one of the functions in the fitness landscape. Is there one that pre-adapts genotypes to evolve equals more easily?

What about co-evolution? Predator-prey dynamics, mutual-isms etc, changing fitness landscape driven by changes in the other species rather than randomly determined.

Planned treatments:
Changing fitness functions (changing the benefit assigned to each function after a certain number of updates, using a range of lengths of time between fitness landscape changes - find optimum?)
Changing mutation rate (evolving under a range of mutation rates, with stable fitness functions - find optimum?)

Questions:
1.How does reproduction work in avida- how far away can they copy themselves? Do they continue to exist after copying?
2. Are we looking for an optimum in a new environment or in a constant environment?
3. Do we want to play with spatial structure? Can spatial structuring impact evolvability, and can we model that in Avida?
4. Can avidians eat each other?


Things to do:
Literature survey on evolvability
-- read
CO Wilke, JL Wang, C Ofria, RE Lenski, C Adami. Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature (2001) vol. 412 (6844) pp. 331-333

Find more applicable papers

Look at the instruction set to see what could be pulled out to decrease modularity

Introduction #3: Erkin Bahçeci

Hi!

I'm Erkin Bahçeci, a sixth year student in Risto Miikkulainen's Neural Networks Research Group at UT Austin. My research interests are multi-agent search, neuroevolution, evolutionary computation, general game playing, and autonomous robots. I'm currently working on a multi-disciplinary project on competitive multi-agent search, modeling innovation search on dynamic fitness landscapes.

As for this project, besides better understanding evolvability in biological evolution, I'm also interested in evolvability from a computer science perspective. Gaining insight into evolvability has the potential to improve the performance and speed of evolutionary methods, which are widely used in computer science. Such insight could include finding out exactly what boosts or reduces evolvability, what makes a population in Avida to increase its evolvability due to evolutionary pressures, and whether general rules of thumb can be derived from these findings, to be applied elsewhere.

Oh, and yeah, that's my 3D reconstructed avatar (complete with Mohawk), ready to help the Avidians behave :)

Introduction #2: Emily Jane McTavish

Hi! I'm Emily Jane,

I'm in my fourth year in the Ecology, Evolution, and Behavior program at UT Austin, in the Hillis lab. I am currently working on a few projects. For my dissertation I am using spatially explicit simulation (DIM SUM) to assess robustness of phylogeographic methods to violations of their assumptions, in particular the impacts of sampling, and of non-gaussian dispersal distribution on inference of population structure. As well I am using 55K SNP data to reconstruct population structure of new world cattle breeds and am investigating genomic patterns of introgression in Texas longhorn cattle. I am particularly excited about applying genomic data to phylogeographic questions, and developing appropriate models of evolution to analyze these data.

Introduction #1: April Wright

Hello all-

I'm April Wright. I am a second year grad student in David Hillis' lab at UT Austin. Broadly, I'm interested in phylogenetics. Making better phylogenies, using phylogenies and communicating to other researchers best practices in the creation of phylogenetic trees.

And that's kind of a weird thing to be interested in. But the way I see it, we, as biologists, use phylogenies for a lot of reasons and to get at and answer a lot of questions. And if we want to have the right answers to those questions, our phylogenies should be accurate to the best of our abilities. Therefore, I want to work on questions not only about increasing accuracy in phylogenies, but getting that information into the hands of people who will use it. In my conversations with people, I've come to believe that there is a fairly profound gap in some fields between the people who do theory and empiricists. I'd like to shrink that gap.

This evolution of evolvability project, obviously, has nothing to do with that. It should be fun anyway. Am I supposed to say anything else?