Welcome to CSE 590CE: Readings and Research in Computational Evolution

1. Welcome to CSE 590CE:Readings and Research in Computational Evolution

2. 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.

3. 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.

4. 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

5. 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 • Instead of post-facto simulations, use compute power to drive the theory, e.g., Hillis (unpublished)

6. 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)

7. 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.

8. 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.

9. 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

10. 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

11. 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