1. Genetic Algorithms Artificial Life

2. Genetic Algorithms Inspired by biology Chromosomes Operations
Contain genes
Describe a entity
algorithm
Solution
Operations
Mutation
Reproduction
Natural selection

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

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

5. 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?

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

7. Ranking Methods Fitness: 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))

8. 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

9. G.A.
Algorithm development
Simulated biology

10. 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?

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

12. 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

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

14. Boppers Rudy Rucker CA world Creatures Turmite Boids – Turboid
Two dimension Turing machine
Boids –
actions based on locations of surrounding boids
No internal state
Turboid
Hybrid of the two

15. Boppers (cont.) Have chromosomes and are G.A.
Chromosomes are highly structured
Various areas of chromosome supply direct aspects of behavior!