1. Evolutionary Computation and Co-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?

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

24. Tron Results

26. Iterated Prisoner’s Dilemma C CD D 3, 30, 5 5, 01, 1 TFT ALL-C ALL-D TFT

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

31. Simulated Hockey

32. Shock Results (single player)

33. Shock Results (one-on-one)

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

35. Brain/Body Co-evolution

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

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