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 a)string of symbols b)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 a)Create boring presentation about TSP b)??? c)Profit

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

8. Choosing Implementation ImplementationAssessment Java Applet / JavaScriptseems popular Server-side (Java/.NET) model and client-side view-controller (JavaScript/HTML) would be awesome Standalone desktop application (C#, Window Forms) could actually work

9. Species

10. Generation of Solution a)select first/last node (using schemata*) b)randomly generate a specie c)test if good (not bad or ugly) d)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?