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