1. Chapter 9 Genetic Algorithms
Evolutionary computation
Prototypical GA
An example: GABIL
Genetic Programming
Individual learning and population evolution
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

2. Evolutionary Computation
1. Computational procedures patterned after biological evolution
2. Search procedure that probabilistically applies search operators to a set of points in the search space
Also popular with optimization folks
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

3. Chapter 9 Genetic Algorithms
Biological Evolution
Lamarck and others:
Species “transmute” over time
Darwin and Wallace:
Consistent, heritable variation among individuals in population
Natural selection of the fittest
Mendel and genetics:
A mechanism for inheriting traits
Genotype  Phenotype mapping
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

4. Chapter 9 Genetic Algorithms
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

5. Representing Hypotheses
(Type=Car  Minivan)  (Tires = Blackwall) by
Type Tires
IF (Type = SUV) THEN (NiceCar = yes)
Type Tires NiceCar
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

6. Operators for Genetic Algorithms
Parent Strings
Offspring
Single Point
Crossover
Two Point
Crossover
Uniform
Crossover
Point
Mutation
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

7. Selecting Most Fit Hypothesis
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

8. Chapter 9 Genetic Algorithms
GABIL (DeJong et al. 1993)
Learn disjunctive set of propositional rules, competitive with C4.5
Fitness:
Fitness(h)=(correct(h))2
Representation:
IF a1=Ta2=F THEN c=T; if a2=T THEN c = F
represented by
a1 a2 c a1 a2 c
Genetic operators: ???
want variable length rule sets
want only well-formed bitstring hypotheses
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

9. Crossover with Variable-Length Bitstrings
Start with
a1 a2 c a1 a2 c
h1 :
h2 :
1. Choose crossover points for h1, e.g., after bits 1,8
h1 : 1[ ]0 0
2. Now restrict points in h2 to those that produce bitstrings with well-defined semantics, e.g.,
<1,3>, <1,8>, <6,8>
If we choose <1,3>:
h2 : 0[1 1]
Result is:
a1 a2 c a1 a2 c a1 a2 c
h3 :
h4 :
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

10. Chapter 9 Genetic Algorithms
GABIL Extensions
Add new genetic operators, applied probabilistically
1. AddAlternative: generalize constraint on ai by changing a 0 to 1
2. DropCondition: generalize constraint on ai by changing every 0 to 1
And, add new field to bit string to determine whether to allow these:
a1 a2 c a1 a2 c AA DC
So now the learning strategy also evolves!
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

11. Chapter 9 Genetic Algorithms
GABIL Results
Performance of GABIL comparable to symbolic rule/tree learning methods C4.5, ID5R, AQ14
Average performance on a set of 12 synthetic problems:
GABIL without AA and DC operators: 92.1% accuracy
GABIL with AA and DC operators: 95.2% accuracy
Symbolic learning methods ranged from 91.2% to 96.6% accuracy
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

12. Chapter 9 Genetic Algorithms
Schemas
How to characterize evolution of population in GA?
Schema=string containing 0, 1, * (“don’t care”)
Typical schema: 10**0*
Instances of above schema: , , …
Characterize population by number of instances representing each possible schema
m(s,t)=number of instances of schema s in population at time t
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms

13. Consider Just Selection
CS 5751 Machine Learning
Chapter 9 Genetic Algorithms