Genetic Search Algorithms Matt Herbster

Why Another Search?  Designed in the 1950s, heavily implemented under John Holland (1970s)  Genetic search is intended to simulate natural systems  Works best on continuous and discrete combinatorial problems  It will only take eight minutes of your time

Definitions  Chromosome  Gene  Allele  Locus  Genotype  Phenotype  String  Feature, character  Feature value  String position  Structure  Parameter set

Characteristics  Reproduction  Crossover  Mutation  Rarely used

Mutation Operations Generative Swap Node Swap Sequence Destructive

Crossover Operations Single pointOrder based

Other Representations  Array, matrix  Tree  String of bits  Any other data structure

Implementation 1.Start with an initial gene pool 2.Generate successors (either randomly or deterministically) to create the first generation pool 3.Each node is evaluated by a fitness function and sorted accordingly 4.Create new generations with the better most likely to reproduce

What is the meaning of the word better?  As determined by fitness function (essentially a heuristic)  Nodes with desired genes are predetermined  Can often approach local maxima rather than the global optimal solution  Assisted by random-restart hill climbing

Variations  Genetic algorithms produce optimal results for many problems, eventually …  Speciation – Two nodes will reproduce only if closely related  Technique helps improve speed  Parallel populations – simulates physical separation with possible migration

Applications  Traveling salesman problem  Drilling of printed circuit boards  Planning bus routes  Scheduling  Computer games – represent an evolution of players’ strategies  Stock market trading – data fitting, trend spotting, budgeting

Questions