Post on 27-Jan-2016
description
Evolutionary Computationand
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
• Fitter schemas increase their representation over time
• Schemas combine like “building blocks”
Evolutionary Issues:
• Representations
• Mutation operators
• Crossover operators
• Fitness functions
Representations• continuous parameters (Schwefel)
• Bit-strings (Holland)
• genetic programs (Koza)
• machine language (Schmidhuber)
• NN building operators (Gruau)
• genotype = phenotype itself
Crossover Operators• one-point
• two-point
• uniform
• special-purpose operators
• mutation only (parthenogenesis)
Fitness Functions
• “deceptive” landscapes– e.g. HIFF (Watson)
– local optima
– premature convergence
• Baldwin effect
• variation over time (robustness)
“Gaps” in the Fossil Record?
“Gaps” in the Fossil Record?
Partial Geographic Isolation
Punctuated Equilibria
“Gaps” in the Fossil Record?• Eldridge & Gould
– partial geographic isolation
– punctuated equilibria
• ideas for Evolutionary Computation?– “island” models
– co-evolution / artificial ecology ?
Co-Evolution• competitive (leapard vs. gazelle)
• co-operative (insects/flowers)
• mixed co-operative/competitive (Maynard-Smith)
• different genes within same genome?
• “diffuse” co-evolution
Sorting Networks
Sorting Networks #1 (Hillis)
• Evolving population of networks
• converged to local optimum
• final network not quite as good as hand-crafted human solution
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)
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)
Virtual Creatures (Sims)
• Evolution– running / skipping / jumping
• Co-evolution– fighting for control of a cube
Tournament Structures
• single species
• multi-species
• all vs. all
• round robin
• all vs. best
Tron
Tron
• population of GP players co-evolve with population of humans over the Internet (Funes, Sklar, Juillé, Pollack)
Tron Results
Tron Results
Iterated Prisoner’s Dilemma
CC D
D3, 3
0, 55, 0 1,
1• TFT ALL-C ALL-D TFT
Collusion
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)
Language Games
Simulated Hockey
Shock Results (single player)
Shock Results (one-on-one)
Evolutionary Robotics• too many generations - robot may get worn
out
• start in simulation, refine on real robot
Brain/Body Co-evolution
Biped Walking
• dynamically stable gait
• evolved parameters
• start on fast approximate simulator
• refine on slow accurate simulator
Future Directions
• massive parallelism
• modularity and evolution
• credit-assignment problem
• society / economic models