{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

6.4.Genetic Algorithms

# 6.4.Genetic Algorithms - Aims This lecture will enable you...

This preview shows pages 1–3. Sign up to view the full content.

11s1: COMP9417 Machine Learning and Data Mining Genetic Algorithms April 5, 2011 Acknowledgement: Material derived from slides for the book Machine Learning, Tom M. Mitchell, McGraw-Hill, 1997 http://www-2.cs.cmu.edu/~tom/mlbook.html Aims This lecture will enable you to describe and reproduce machine learning approaches using genetic algorithms. Following it you should be able to: outline the framework of evolutionary computation reproduce the prototypical genetic algorithm for machine learning design representations for rule learning by a genetic algorithm describe genetic algorithm operators such as mutation and crossover outline the schema theorem describe genetic programming define the Baldwin e ff ect [Recommended reading: Mitchell, Chapter 9] [Recommended exercises: 9.1 (9.2-9.4) ] COMP9417: April 5, 2011 Genetic Algorithms: Slide 1 Evolutionary Computation Computational procedures patterned after biological evolution Search method that probabilistically applies operators to set of points in the search space Can be viewed as form of stochastic optimization aim to find approximate solutions to di cult optimization problems COMP9417: April 5, 2011 Genetic Algorithms: Slide 2 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 mapping: genotype phenotype COMP9417: April 5, 2011 Genetic Algorithms: Slide 3

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
A Genetic Algorithm for Machine Learning GA ( Fitness, Fitness threshold, p, r, m ) Initialize: P p random hypotheses Evaluate: for each h in P , compute Fitness ( h ) While [max h Fitness ( h )] < Fitness threshold Do 1. Select: Probabilistically select (1 r ) p members of P to add to P S . Pr( h i ) = F itness ( h i ) p j =1 F itness ( h j ) 2. Crossover: Probabilistically select r · p 2 pairs of hypotheses from P . For each pair, h 1 , h 2 , produce two o ff spring by applying the Crossover operator. Add all o ff spring to P s . 3. Mutate: Invert a randomly selected bit in m · p random members of P s 4. Update: P P s 5. Evaluate: for each h in P , compute Fitness ( h ) Return hypothesis from P with highest fitness. COMP9417: April 5, 2011 Genetic Algorithms: Slide 4 Representing Hypotheses for Genetic Algorithms Represent ( Outlook = Overcast Rain ) ( Wind = Strong ) by Outlook Wind 011 10 Represent IF Wind = Strong THEN PlayTennis = yes by Outlook Wind PlayTennis 111 10 10 COMP9417: April 5, 2011 Genetic Algorithms: Slide 5 Operators for Genetic Algorithms Single-point crossover: 11101001000 00001 010101 11111000000 11101010101 Initial strings Crossover Mask Offspring Two-point crossover: 11 101001000 0000101 0101 00111110000 11001011000 10011010011 Uniform crossover: Point mutation: 111 010 010 00 0 0001 01 0101 10001000100 111010 01000 111010 11000 00101000101 00001001000 01101011001 COMP9417: April 5, 2011 Genetic Algorithms: Slide 6 Operators for Genetic Algorithms Mutation – new version of single parent Crossover – two new o ff spring from two parents Parameters for operators chosen randomly at each application
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 1

6.4.Genetic Algorithms - Aims This lecture will enable you...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online