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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.

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Genetic Algorithms

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  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?

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