Transcript The Use of Linkage Learning in Genetic Algorithms

The Use of Linkage Learning
in Genetic Algorithms
By David Newman
Genetic Algorithms: Recap

Search Algorithm that uses Mechanisms of
Natural Selection
– Parameter Sets (Genomes) have Fitness Values
– Higher Fitness Value = Higher Probability of
Selection

Selected Genomes used to produce Next
Generation
–
–
–
–
Directly Copied
Mutation
Crossover between Two Genomes
Mutation & Crossover
Linkage Learning

Why Learn Linkage?
– Reduces the Probability that sets of Functional
Dependent Values are split up when Crossover is
performed

What is Linkage Learning?
– The Ability to Learn Functional Dependency
between Genes

How is Linkage Learnt?
– Improving Genetic Linkage
• Distance between Functionally Dependent Genes
– Store Functionally Dependent Relationships
Linkage Learning GAs

Messy GA (mGA)

Incremental Commitment GA (ICGA)

BOA (Bayesian Optimization Algorithm)

Hierarchical BOA (hBOA)

Harik’s “Learning Linkage” GA