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Evolutionary Computation and Co-evolution

Evolutionary Computation and Co-evolution. Alan Blair October 2005. Overview. Evolutionary Computation. population of individuals fitness function repeat cycle of: evaluation, selection, crossover/mutation. Bit-String Operators:. Schema “Theorem”. Implicit Parallelism

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Evolutionary Computation and Co-evolution

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  1. Evolutionary ComputationandCo-evolution • Alan Blair • October 2005

  2. Overview

  3. Evolutionary Computation • population of individuals • fitness function • repeat cycle of: • evaluation, • selection, • crossover/mutation

  4. Bit-String Operators:

  5. Schema “Theorem” • Implicit Parallelism • Fitter schemas increase their representation over time • Schemas combine like “building blocks”

  6. Evolutionary Issues: • Representations • Mutation operators • Crossover operators • Fitness functions

  7. Representations • continuous parameters (Schwefel) • Bit-strings (Holland) • genetic programs (Koza) • machine language (Schmidhuber) • NN building operators (Gruau) • genotype = phenotype itself

  8. Crossover Operators • one-point • two-point • uniform • special-purpose operators • mutation only (parthenogenesis)

  9. Fitness Functions • “deceptive” landscapes • e.g. HIFF (Watson) • local optima • premature convergence • Baldwin effect • variation over time (robustness)

  10. “Gaps” in the Fossil Record?

  11. “Gaps” in the Fossil Record?

  12. Partial Geographic Isolation

  13. Punctuated Equilibria

  14. “Gaps” in the Fossil Record? • Eldridge & Gould • partial geographic isolation • punctuated equilibria • ideas for Evolutionary Computation? • “island” models • co-evolution / artificial ecology ?

  15. Co-Evolution • competitive (leapard vs. gazelle) • co-operative (insects/flowers) • mixed co-operative/competitive (Maynard-Smith) • different genes within same genome? • “diffuse” co-evolution

  16. Sorting Networks

  17. Sorting Networks #1 (Hillis) • Evolving population of networks • converged to local optimum • final network not quite as good as hand-crafted human solution

  18. Sorting Networks #2 (Hillis) • two co-evolving populations (networks and strings) • can escape from local optima • punctuated equilibria observed • better than hand-crafted solution (Tufts, Juillé & Pollack)

  19. Co-evolutionary Paradigms • machine vs. machine (Sims) • human vs. machine (Tron) • mixed co-operative/competitive (IPD) • language games (Tonkes, Ficici) • single individual ? (Backgammon) • brain / body (Sims, Hornby, Lipson)

  20. Virtual Creatures (Sims) • Evolution • running / skipping / jumping • Co-evolution • fighting for control of a cube

  21. Tournament Structures • single species • multi-species • all vs. all • round robin • all vs. best

  22. Tron

  23. Tron • population of GP players co-evolve with population of humans over the Internet (Funes, Sklar, Juillé, Pollack)

  24. Tron Results

  25. Tron Results

  26. Iterated Prisoner’s Dilemma C D • TFT ALL-C ALL-D TFT C 3, 3 0, 5 5, 0 1, 1 D

  27. Collusion

  28. Meta-Game of Learning • Co-evolution tends to provide an opponent of appropriate ability • generally helps to escape local optima • however, can create new “mediocre stable states” (collusion)

  29. Language Games

  30. Simulated Hockey

  31. Shock Results (single player)

  32. Shock Results (one-on-one)

  33. Evolutionary Robotics • too many generations - robot may get worn out • start in simulation, refine on real robot

  34. Brain/Body Co-evolution

  35. Biped Walking • dynamically stable gait • evolved parameters • start on fast approximate simulator • refine on slow accurate simulator

  36. Future Directions • massive parallelism • modularity and evolution • credit-assignment problem • society / economic models

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