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 Instead of post-facto simulations, use compute power to drive the theory, e.g., Hillis (unpublished)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 OrganismsEvolution, 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)Adaptive Radiation from Resource Competition in Digital Organisms 2/7: Evolution of Biological Complexity;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