Evolvability

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Evolvability

  1. 1. Biologically inspired design of design Ben Bolker Departments of Mathematics & Statistics and Biology, McMaster University 19 December 2010Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 1 / 20
  2. 2. 1 Introduction 2 Biologically inspired optimization 3 Avenues for exploration/conclusionsBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 2 / 20
  3. 3. Outline 1 Introduction 2 Biologically inspired optimization 3 Avenues for exploration/conclusionsBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 3 / 20
  4. 4. Biologically inspired design Examples: micro/macro fluid dynamics: kingfisher beaks, robot fish, sharkskin materials (Velcro, gecko toes) structural color Some references: http://www.japanfs.org/en_/newsletter/200503-2.html, http://www.treehugger.com http://brainz.org/15-coolest-cases-biomimicry/Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 4 / 20
  5. 5. Evolutionary computation Biologically inspired design of design: i.e., biologically inspired algorithms Can we learn from evolutionary biology? How? Generative systems (Genr8, Maya, Rhino . . . )Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 5 / 20
  6. 6. Evolutionary computation Biologically inspired design of design: i.e., biologically inspired algorithms Can we learn from evolutionary biology? How? Generative systems (Genr8, Maya, Rhino . . . )Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 5 / 20
  7. 7. Problem: bridge design http://imac.epfl.ch/Team/landolf/Rhode%20et%20al%20EG-ICE%2009.pdf objective function: cost, performance parameter space: area of layer and x-cables; outer diameter, diameter-to-thickness ratio of tubular struts; self-stress of layer and x-cablesBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 6 / 20
  8. 8. Problem: patio design Caldas (2008) doi:10.1016/j.aei.2007.08.012 objective function: (?) parameter space: which sides have balconies (24 possibilities, encoded as a bit string): discreteBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 7 / 20
  9. 9. Parameter space http://www.iread.it/lz/hypercube.htmlBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 8 / 20
  10. 10. Outline 1 Introduction 2 Biologically inspired optimization 3 Avenues for exploration/conclusionsBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 9 / 20
  11. 11. Adaptive landscapes Wright 1931 (from Johnson 2008)Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 10 / 20
  12. 12. “No free lunch” theorem Across all all possible optimization problems, all optimization algorithms perform equally: none is universally best . . . a “good” optimization algorithm is only good for some particular problems http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization Ho (2002) http://resolver.scholarsportal.info/resolve/00223239/v115i0003/549_seotntaii.xmlBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 11 / 20
  13. 13. Consequences of NFL for biologically inspired design Question Does biological evolution use good optimization techniques?Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 12 / 20
  14. 14. Consequences of NFL for biologically inspired design Question / ///// / / / / / / / / / / / / / / /// good/////////////////// techniques? Does/biological/evolution/use//////// optimization///////// / / / / / ////// ////// / //// Do evolving systems face the same kinds of problems we do?Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 12 / 20
  15. 15. characteristics of objective functions/landscapes discrete vs continuous single vs multiple peaks smooth vs jaggedBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 13 / 20
  16. 16. Selection Evolution occurs in populations Offspring have different characteristics Best ones survive, the population “climbs the hill”Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 14 / 20
  17. 17. mutation In order to move (and get out of local minima), need to maintain variation: mutation too little mutation: slow movement too much: constantly losing fitness Selection+mutation = “asexual reproduction” http://en.wikipedia.org/wiki/TMNTBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 15 / 20
  18. 18. crossover/recombination let individuals “mate” randomly select some characteristics from each parent combines features of two different solutions: building blocks hypothesis tradeoff: can also break up good combinations http://www.flickr.com/photos/ajc1/ modularity is important 1103490291/sizes/o/Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 16 / 20
  19. 19. specific algorithms 1 10 q q q q q genetic algorithms q q q qq q q q q qq q q qq qq q qq qq q q translate numeric parameters qq q qq q q q q q q q q q qq q q q q q q q q q qq q qq q qq q q q q q q q q into a bit string (e.g. 0011) q q q q q q q q qq q q q q qq q q q qq good for discrete problems q q q q q q does not respect module 50 999 boundaries q q q q q q differential evolution q q q q q q q q q q q q q q q q qq q q q q q q q q q numeric parameters as q separate “genes” different mutation operation — uses direction informationBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 17 / 20
  20. 20. Outline 1 Introduction 2 Biologically inspired optimization 3 Avenues for exploration/conclusionsBen Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 18 / 20
  21. 21. genetic complications (opportunities?) genetic structure: chromosomes, gene clusters non-point mutations: deletion, duplication mating types (♂, ♀) modifiers: dominance, canalization genotype-phenotype map: integrating developmental biology (back to generative systems) Which of these are important for optimization, and which are accidents? (How and why did they evolve?)Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 19 / 20
  22. 22. evolving complexity closing the loop: development + evolution can we allow for evolution of complexity (evolving grammars)? evolution of modularity (adaptive recombination, gene rearrangement) It’s cool, but is it worth it?Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 20 / 20
  23. 23. tradeoffs general vs. problem-specific solutions (NFL) performance vs robustness (both in optimization algorithms and solutions) programming vs computation time computation vs “meta-computation”Ben Bolker (Departments of Mathematics & Statistics and Biology, McMaster University) Evolutionary computation 19 December 2010 21 / 20

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