2. 2 What Genetic Programming ISN'T: What Genetic Programming ISN'T: Engineering a breed of purple carrots. Engineering a breed of purple carrots. Analyzing the human genome Analyzing the human genome A way to create smarter humans. A way to create smarter humans. What Genetic Programming IS: What Genetic Programming IS: A way to create smarter computers. A way to create smarter computers. Fun. Fun.

3. 3 A big Q in A.I. "How can computers learn to solve problems without being explicitly programmed?"  Arthur Samuel (1959)  Arthur Samuel (1959) 0 0 1 0 1 0 1 1

4. 4 The GP Guide to Evolution Charles Darwin

5. 5 Charles Darwin

6. 6 Gregor Mendel “The Father of Genetics”

7. 7 “The Code is Alive”

8. 8 Basic Steps of GP 1. Populate Generation 0 with random computer programs. 1. Evaluate how “fit” each program is. 2. Is the “best-so-far” program “fit enough”? 3. Use the “fitness” values to determine which programs will produce offspring, to create the Next Generation. 2. Output the “best-so-far” program.

9. 9 Preparation Program Ingredients Program Ingredients (math? “rt 90”? “ifelse”? “fd 1.0”?) (math? “rt 90”? “ifelse”? “fd 1.0”?) Fitness Measure Fitness Measure How good (attractive) is a program (creature)? How good (attractive) is a program (creature)? Parameters Parameters (e.g. pop. size, mutation probability, etc.) (e.g. pop. size, mutation probability, etc.)

10. 10 Example: Symbolic Regression Given: Some points Find: f(x) = x 2 + x + 1 Ingredients {x, random constants between -5.0 and 5.0} {+, -, *, (protected division) } A “program” is a math function.

11. 11 Into the DNA Forest… Generation 0 A program is just a tree. Pop. Size = 4 (x + 1) – 01 + (x * x)2 + 0x * (-1 – (-2))

12. 12 Fitness Measure.671.001.702.67

13. 13 The Birds and the Bytes… 3 Fundamental Genetic Operations 3 Fundamental Genetic Operations Cloning (Asexual Reproduction) Cloning (Asexual Reproduction) Mutation Mutation Crossover (Recombination) Crossover (Recombination)

14. 14 Cloning Fitness =.67 Generation 0 Generation 1 COPY Fitness =.67

15. 15 Mutation Fitness = 1.70 Generation 0 Generation 1 Mutate Fitness = ?

16. 16 Crossover Crossover Generation 0Generation 1

17. 17 Gen 0  Gen 1 Copy Mutate Crossover

18. 18 On to the turtles... Genetic Programming in NetLogo...