overview presentation
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Welcome to CSE 590CE:Readings and Research in Computational Evolution
Course Mechanics
Mondays 1:30 to 2:20 1/17 and 2/21 are holidays = 8 meetings
Today’s organizational 7 paper discussion meetings
One normal or two small papers per week. Course web site to be set soon. Paper presenters should plan on a 30 minute
presentation: 20 slides.
About the Instructor
Daniel Weise M.S. ’82, PhD ’86 MIT A.I. Lab Stanford faculty 86-92 Microsoft Research 92-04 Affiliate Faculty (RSN) UW CSE I’m a CS type learning about biology, cells,
evolution, biochemistry, genetics, ecology, genomics, proteomics, metabolomics, etc.
We are here to learn and think We all get to learn together All comments and insights on papers are
welcome and encouraged I want this to be a discussion course.
I hope we have a diversity of backgrounds and approaches in this room to help ensure we don’t end up in group think
Computational Evolution
It’s about simulation. Computer power per unit cost is still exploding
exponentially. Can we use this power to create simulations that
shed insight in biological processes? What about the compute power available in ten
years? Instead of post-facto simulations, use compute
power to drive the theory, e.g., Hillis (unpublished)
Computational Evolution:Self replication + variation + landscapes Computational models of self-replicating
organisms Digital (Von Neumann architecture) Molecular (communicating processes)
Simulated landscapes with niches. Landscapes provide “fitness” measures
Subject to mutation and variation (diploid)
Building Phenotypes is the Fundamental Problem in Computational Evolution Selection operates on the phenotypes of
organisms. Phenotypes come from physics Modeling physics is expensive
Approximations Relating phenotypes back to biology is tricky.
What can we hope to find?
Validation of existing theories/hypotheses. The ability to propose and test new
hypotheses. Unanticipated phenomena to look for in
nature (e.g., Hillis) Better models for the physical world. Recapitulation of the rise of complexity of
organisms.
CE is at intersection of many fields Population/Evolutionary Genetics
Computes how gene frequencies of populations change due to selection, migration, & mutation.
Ecology When organisms can interact, ecologies form.
Efficient simulation methods Nature had 10^9 years and 10^28 organisms
Biochemistry and biophysics When modeling at the molecular level
Artificial Life, Signal Processing, Information Theory, Program Analysis
Readings
1/10: Evolution, Ecology and Optimization of Digital Organisms
1/17: Holiday, no class. 1/24: The Evolutionary Origin of Complex Adaptive Features 1/31:
Adaptive Radiation from Resource Competition in Digital Organisms (2004)
2/7: Evolution of Biological Complexity; 2/14: Tentative: four short Avida papers. 2/21: Holiday, no class. 2/28: TBA 3/07: TBA
Fun Reading Artificial Life by Steven Levy, Vintage books Proceedings of the 2nd Artificial Life conf. Introduction to Artificial Life, Chris Adami,
Telos books Theoretical Evolutionary Genetics, Joseph
Felsenstein, online at his website The Philosophy of Artificial Life, Margaret
Boden, Oxford Press Anything by Dawkins, Gould, or Maynard
Smith