Genetic Algorithms Artificial Life

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

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

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

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?

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

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))

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

G.A. Algorithm development Simulated biology

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?

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

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

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

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

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