Announcement
What: Resume Writing Workshop
When: 4:10 p.m. - 5:00 p.m. on Friday, January 20th
Where: 218 Roberts
Who: Ruth Kronfuss, Director of Human Resources at Zoot Enterprises
Sponsor: Local ACM Chapter
Chapter 2: GAs in Problem Solving
Section 2.1: Evolving Computer Programs
Evolving Lisp Programs
Genetic Programming or GP
- John Koza
- Parse trees are used to represent Lisp code. For example,
(sqrt (+ (* x x) (* y y)))
- Steps
- Choose a set of functions and terminals
- Generate a syntactically correct initial population of parse trees
- Evaluate the fitness of each program
- Copy the most fit 10% into the next generation, produce the other
90% by using fitness based selection with single point crossover
(no mutation due to large initial population)
- Go to step 3
Blocks World
- Koza
- Sensors: CS, TB, NN
- Functions: MS(x), MT(x), DU(exp-1, exp-2), NOT(exp), EQ(exp-1, exp-2)
- 166 fitness cases
- Population size of 300
Evolving Cellular Automata
- Crutchfield, Das, Hraber, Mitchell
- To study how natural evolution creates sytems
that enable emergent computation
- To study how to engineer emergent computation in
decentralized multiprocessor systems
- 1-D CAs typically "wrap around"
- A rule takes as input the current cell and the other cells in
its radius, r
- Space time diagram, Figure 2.6
Task
- Decide within M time steps whether an initial configuration of
N bits contains a majority of 1s with r = 3.
- In this case the critical density, pc = 0.5
- Space time diagrams for the majority rule, Figure 2.7
- A chromosome has a bit length of 22r+1 = 27 = 128.
Thus, the search space size is 2128
- N = 149
- Population size = 100
- Steps
- Generate a random initial population
- Select 100 new initial configurations over a uniform distribution
of density
- Calculate the fitness for each rule
- Copy the 20 most fit rules into the next generation
- Form the other 80 rules by randomly selecting 2 of the 20
selected rules and then performing single point crossover.
Mutate each offspring twice.
- Go to step 2
- PN(rule): the percent of
10000 initial configurations generated from an
unbiased distribution correctly classified within 2N timesteps
- Measured values of PN, Table 2.1
- More space time diagrams, Figure 2.8