1. Evolutionary Algorithms

2. Techniques that harness evolution
Broad range of applications
Genetic algorithms
Darwinian Process
A characteristic pattern
Pattern can be copied
Occasional variations
Competition for limited territory
Multifaceted environment
Next round of variations based on success

3. Definitions Genotype Phenotype DNA Mutation (copy errors)
Fitness Function

4. Genetic Representation
Phenotype == Genotype
Discrete, real, tree
Growth and cellular differentiation

5. Genetic operators Crossover Mutation One point Multiple point
P for a location is 0.01 (higher than nature)

6. Winston’s example Kookie
flour
sugar
Chromosome:
Mutation – choose a gene and change it
Crossover –
Fitness:
Probability of survival related to quality of the cookie

7. Algorithm Create a population
Mutate one or more genes producing new offspring
Mate one or more pairs
Remove from population by fitness
Iterate!

8. Design constraints Number of chromosomes in population Mutation rate
Cost of large numbers
Low numbers slow evolution
Mutation rate
Too high can cause out of control systems
Mating rules
Fitness, proximity
Can chromosome have multiple occurrences in a population?

9. With Kookie Selection rule It will work with mutation alone
Higher qualities survive
Randomly keep a small number unrelated to quality
It will work with mutation alone
Adding crossover improves convergence

10. Ranking Methods Fitness as relative quality: Rank Method
Given qi as the quality measure of the i’th entity
Fi = qi / (Sj qj)
Rank Method
Eliminates bias toward the “best” and reduces bias due to measurement scale
Rank the candidates by fitness
Choose a probability, p, of choosing the highest ranking
Pi = p * (1.00 – ( P1 + P2 + P3 …Pi-1))

11. Ranking (cont.) Survival of the Most Diverse Need a diversity measure
It is good to be different!
Need a diversity measure
Sort by the combination of diversity and quality
The use rank method

12. Examples See Kosa Digital Design – encode FSA Analog Design
Tree of build instructions
Components as Genes:
type
Values
Connections

13. CHC Algorithm Larry J. Eshelman
Use crossover and elitist selection
When converged population appears, then mutate! (Cataclysm)

14. Artificial Life Study aspects of “life” in a simulated environment
Allow experiments we cannot do in real life
Allows us to abstract from life and life properties
Grow AI?

15. Self Replication John Von Neumann (1951) Cellular Automata Life –
Turing computable
Self replicator

16. Tierra System Thomas Ray
Organism are built of instructions is a ”core”
32 instructions encoded in 5 bits
Work by pattern matching
Simulated enzymes and proteins

17. Spontaneous Generation
Andrew Paragellis
Instruction sequences
Mutation
Random seeding
Self replicators emerged

18. Artificial Life Lab Rudy Rucker
Boppers
Artificial Life Lab
Rudy Rucker

19. Boppers Simple bug-like creatures Turing Machine based
<current state, read color, write color, new state, turn>
Cycle is
move in current direction
Compute next state, write color and turn

20. Creatures Turmite Boids – Turboid Two dimension Turing machine
actions based on locations of surrounding boids
No internal state
Turboid
Hybrid of the two

21. Genetics Have chromosomes and are G.A.
Chromosomes are highly structured
Various areas of chromosome supply direct aspects of behavior!

22. Windrose

23. Turmite DNA IQ = number of states Turn table - size of windrose
State (indexed by read color):
Next State Table (8 * IQ)
Turn Table (8 * IQ)
Write Color Table (8 * IQ)
Total size is 24 * IQ

24. Turmite Enzymes Body icon Trail Nodes Trail lines Base speed
Bopper type
Windrose IQ

25. Boids Based on Reynolds Boid Flocking rules Collision Avoidance
Velocity Matching
Flock Centering

26. Boid DNA 18 bytes for 11 varibles 4 byte size integers:
WatchTwoFlag = watch one or two closest
MaxSpeed
CruiseDistance - distance from flock mate
NearWallDistance - closest distance allowed

27. Boid DNA (cont.) 7 real parameters RealSpeed
CopyWeight = fly parallel to neighbor
CenterWeight = move to center of flock
AttackWeight = attack other flock
FleeWeight = run from other flock
VeerWeight = fly away from wall
Acceleration

28. TurBoid Features of both! DNA is like turmite First 18 bytes of DNA
Turmite state table
Boid parameters

29. Summary