1 / 18

Genetic Algorithms

Genetic Algorithms. Siddhartha K. Shakya School of Computing. The Robert Gordon University Aberdeen, UK ss@comp.rgu.ac.uk. What are GAs?. Class of Evolutionary algorithm Based on Darwinian Evolution First proposed by John Holland 1960s. 1970s

mwhitley
Télécharger la présentation

Genetic Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Genetic Algorithms Siddhartha K. Shakya School of Computing. The Robert Gordon University Aberdeen, UK ss@comp.rgu.ac.uk

  2. What are GAs? • Class of Evolutionary algorithm • Based on Darwinian Evolution • First proposed by John Holland 1960s. 1970s • Used to solve wide verity of Optimization problems occurring in engineering Science and Business.

  3. What is an Optimization Problem? • Is to find optimum or near optimum solution from the possible set of candidate solution • Search through big solution space to find best solution • There is some scoring criteria for evaluating goodness of solution also called fitness of solution

  4. a d b e c f Example: Graph colouring problem (GCP) Given a set of colours, GCP is to try and assign Colour to each nodes in such the way that neighbouring nodes will not have same colour

  5. What is a solution and a fitness? • Solution is a set of colours assigned to the nodes of graph. • Fitness is the number of correctly coloured nodes.

  6. a 1 1 1 1 d b e 0 0 0 0 1 0 0 1 1 1 1 1 c 0 0 1 0 0 1 0 0 a f d b e a d c a b e d a f b e c d b e c f c f f What is a solution and a fitness? Given 2 colour Black = 0 White = 1 Fitness = 0 Fitness = 1 Fitness = 6 Fitness = 2

  7. Graph Colouring Problem (GCP) • Looks very simple • But as the size of the graph grows, it becomes increasingly difficult. • Lots of different problems can be see as a GCP • One of the approach to solve GCP is GA

  8. 1 1 0 0 1 0 1 1 0 1 0 1 fitness 1 a a a b c d e f d d b b e e 1 0 0 1 1 1 c c f f 1 0 1 0 1 0 6 Representation of Problem in GA • Solution is usually represented as a bit string called chromosome

  9. Work flow of GA • Initialization of parent population • Evaluation • Selection • Crossover/recombination • Mutation • Evaluate child and Go to step 3 until termination criteria satisfies

  10. 1 0 1 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 0 1 0 0 1 1 Initialization of parent population • Generate the M number of solution string known as parent population • Mostly random

  11. a d b e c Parent population fitness f 1 0 1 1 0 1 2 0 0 1 0 1 1 2 1 0 1 0 1 1 4 0 1 0 0 1 1 3 Evaluation • Give fitness to each of the solution

  12. Parent population fitness 1 0 1 1 0 1 2 selection fitness 0 0 1 0 1 1 2 1 0 1 0 1 1 4 1 0 1 0 1 1 4 0 1 0 0 1 1 3 0 1 0 0 1 1 3 0 1 0 0 1 1 3 1 0 1 1 0 1 4 Selection • Fit solution are likely to survive and bad solution are likely to die off • Select some of the best fit chromosomes from parent population according some selection criteria (eg. Roulette wheel selection)

  13. Selected Solution 1 0 1 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 Crossover/Recombination • Exchange partial solution between pair of selected solution with some probability value eg 70% Child Population After Crossover

  14. Child Population After Crossover 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 Mutation • Change the value of an allele of solution with some small probability value eg 1% • Motivation is to explore new point in the solution space Child Population After mutation

  15. a d b e c f Replaced Parent Population 1 0 0 0 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 0 1 1 1 Replace with parent population and repeat process • Evaluate child population and replace parent population • Go to selection step and repeat the process until termination criteria satisfies • Eg. Exit after given number of iteration finishes fitness 1 2 4 6

  16. Selected Solution Parent population fitness Crossover Selection 1 0 1 1 0 1 2 0 0 1 0 1 1 2 Repeat iteration 1 0 1 0 1 1 4 After mutation 1 0 fitness 0 0 1 0 1 0 1 0 1 1 a 0 1 0 0 1 1 3 0 1 0 0 1 1 1 b d Mutation e 0 1 0 0 1 2 1 c 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1 1 0 1 1 1 1 0 1 0 0 0 1 1 0 1 0 0 1 1 1 0 0 0 1 1 6 1 1 1 1 1 1 1 1 Given 2 colours (0,1) f 4 Putting Everything together Initialization Evaluation After Crossover

  17. Conclusion • Different selection, crossover and mutation operators exists. • Different GAs designed for different problems • Hybridise GA with other search algorithms • Probabilistic Model Building Genetic Algorithms (PMBGAs)

  18. Links • Introductory tutorial on GA By D.E Goldberg http://online.engr.uiuc.edu/shortcourses/innovation/index.html • GA Archive http://www.aic.nrl.navy.mil/galist/ Introductory Books • David E. Goldberg. 1989. Genetic Algorithms in search, optimization and machine learning. Addison-Wesley.0-201-15767-5 • Mitchell, M. (1998). An Introduction to genetic Algorithm. MIT Press. 0-262-113316-4(HB)

More Related