{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

ga1 - Geneticalgorithms Introduction...

Info icon This preview shows pages 1–13. Sign up to view the full content.

View Full Document Right Arrow Icon
Genetic algorithms Introduction [email protected]
Image of page 1

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

View Full Document Right Arrow Icon
Genetic Algorithms in a slide Premise Evolution worked once (it produced us!), it might work again Basics Pool of solutions Mate existing solutions to produce new solutions Mutate current solutions for long-term diversity Cull population  
Image of page 2
Originator John Holland Seminal work Adaptation in Natural and Artificial Systems introduced main GA  concepts, 1975
Image of page 3

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

View Full Document Right Arrow Icon
Introduction Computing pioneers (especially in AI) looked to natural systems as  guiding metaphors Evolutionary computation Any biologically-motivated computing activity simulating natural  evolution Genetic Algorithms are one form of this activity Original goals Formal study of the phenomenon of adaptation John Holland An optimization tool for engineering problems
Image of page 4
Main idea Take a population of candidate solutions to a given problem Use operators inspired by the mechanisms of natural genetic variation Apply selective pressure toward certain properties Evolve a more fit solution 
Image of page 5

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

View Full Document Right Arrow Icon
Why evolution as a metaphor Ability to efficiently guide a search through a large solution space Ability to adapt solutions to changing environments “Emergent” behavior is the goal “The hoped-for emergent behavior is the design of high-quality  solutions to difficult problems and the ability to adapt these  solutions in the face of a changing environment”  Melanie Mitchell, An Introduction to Genetic Algorithms
Image of page 6
Evolutionary terminology Abstractions imported from biology Chromosomes, Genes, Alleles Fitness, Selection Crossover, Mutation
Image of page 7

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

View Full Document Right Arrow Icon
GA terminology In the spirit – but not the letter – of  biology GA chromosomes are strings of genes Each gene has a number of alleles; i.e., settings Each chromosome is an encoding of a solution to a problem A population of such chromosomes is operated on by a GA
Image of page 8
Components of a GA A problem to solve, and ... Encoding technique        ( gene, chromosome ) Initialization procedure                 (creation) Evaluation function                  (environment) Selection of parents                (reproduction) Genetic operators     (mutation, recombination) Parameter settings              (practice and art)
Image of page 9

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

View Full Document Right Arrow Icon
Simple Genetic Algorithm { initialize population; evaluate population; while TerminationCriteriaNotSatisfied { select parents for reproduction; perform recombination and mutation; evaluate population; } }
Image of page 10
The GA Cycle of Reproduction reproduction population evaluation modification discard deleted members parents children modified children evaluated children
Image of page 11

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

View Full Document Right Arrow Icon
Population
Image of page 12
Image of page 13
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern