1 / 20

Genetic Algorithms

Genetic Algorithms. CS460: Capstone Experience Project Sergii S. Bilokhatniuk. Project. Simulate genetic algorithms and analyze effects of mutations General Requirement Develop a gentle tutorial for the concept of genetic algorithms. Pick an existing program and modify it.

tibor
Download Presentation

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 CS460: Capstone Experience Project Sergii S. Bilokhatniuk

  2. Project • Simulate genetic algorithms and analyze effects of mutations • General Requirement • Develop a gentle tutorial for the concept of genetic algorithms. • Pick an existing program and modify it. • The system graphically displays the state of each generation with appropriate statistics that show progress toward the goal. • The system should allow dynamic modification of parameters, operators, and probabilities. • Add your own genetic operators based on your analysis. • Pick a new problem and create a genetic solution by mutating populations. The problem should be NP-complete and your results should be compared analytically to a known algorithm that approximates a solution.

  3. Potential Applications of GA virtually anything where potential solution is • string of symbols • testable for fitness • Generating automatons • Finding routes • Constructing formulas • Writing War & Peace (not really) • …

  4. Choosing the Problem Traveling Salesman Problem (TSP): Given a list of cities and a map of the roads • visit each city once, • come back to hometown • use the shortest route.

  5. TSP, Domain and Range • Input: Map • Output: Path

  6. TSP Solution Process • Create boring presentation about TSP • ??? • Profit

  7. TSP Solution Process • Create initial population of routes • Assess fitness of each route • If not satisfactory, create new population • Introduce mutation (optional) • Goto b)

  8. Choosing Implementation

  9. Species

  10. Generation of Solution • select first/last node (using schemata*) • randomly generate a specie • test if good (not bad or ugly) • Repeat * — zero/one mask

  11. Procreation • Schema One • Creates good/bad/ugly • Small rate of success

  12. More Procreation • Schema Two • Greater rate of success • Creates Good/Ugly

  13. Mutation • Schema One, Random • Generates mostly Bad/Ugly (for not-connected graph)

  14. More Mutation • Schema Two, Selective • Generates Good/Ugly

  15. Fitness • Simple comparer • Maximum • Minimum • Average

  16. Population Control • Elitism Rate • % of population selected to be carried over to next generation without change • Elite gets to procreate too • Discard same % of least performing part of population • Mutation Rate • % of genes of each new specie that get mutated

  17. Process

  18. Demo “Let There Be Algorithms…”

  19. What I have learned It was all worth it.

  20. Thanks! I hope it went well • Question? • Suggestions? • Job Offers?

More Related