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Understanding Genetic Algorithms: Applications, Strengths, and Weaknesses

Genetic Algorithms (GAs) are a subset of artificial intelligence inspired by Darwinian evolution. They utilize principles of natural selection and genetics to solve complex optimization problems through processes such as selection, crossover, and mutation. While GAs have notable strengths like parallel processing and the ability to tackle multi-parameter problems, they also face weaknesses, including premature convergence and inefficiencies in certain scenarios. Applications of GAs span various fields, including police sketching, scheduling, aerospace engineering, and medical research.

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Understanding Genetic Algorithms: Applications, Strengths, and Weaknesses

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Presentation Transcript

  1. What good are they? Genetic Algorithms

  2. What are Genetic Algorithms? • GAs

  3. What are Genetic Algorithms? • GAs • Subset of Artificial Intelligence

  4. What are Genetic Algorithms? • GAs • Subset of Artificial Intelligence • Based on Darwinism

  5. Weaknesses • DNA representation

  6. Weaknesses • DNA representation • Fitness function

  7. Weaknesses • DNA representation • Fitness function • Premature convergence

  8. Weaknesses • DNA representation • Fitness function • Premature convergence • Stagnation

  9. Weaknesses • DNA representation • Fitness function • Premature convergence • Stagnation • Non-optimal

  10. Weaknesses • DNA representation • Fitness function • Premature convergence • Stagnation • Non-optimal • Slower in certain instances

  11. Weaknesses • DNA representation • Fitness function • Premature convergence • Stagnation • Non-optimal • Slower in certain instances • Irreducible complexity

  12. Strengths • Parallel

  13. Strengths • Parallel • Exhaustive

  14. Strengths • Parallel • Exhaustive • Multi-parameter

  15. Strengths • Parallel • Exhaustive • Multi-parameter • Ignorant

  16. What is it good for? • Police Sketching

  17. Police Sketching

  18. What is it good for? • Police Sketching

  19. What is it good for? • Police Sketching • Scheduling and Routing • Timetable problem – 40% improvement backtracking heuristic • Networks and Cell phone • Satellites • Airports – 3-4 more flights per hour • Spirits Distributers – United Distillers and Vintners • Car manufacturing

  20. What is it good for? • Police Sketching • Scheduling and Routing • Acoustics • Grosser Musikvereinsaal in Vienna

  21. What is it good for? • Police Sketching • Scheduling and Routing • Acoustics • Aerospace Engineering • Wing for supersonic aircraft vs. SST • Drag (supersonic) • Drag (subsonic) • Load • Twisting

  22. Aerospace Engineering • Dampening • Twisting

  23. What is it good for? • Scheduling and Routing • Acoustics • Aerospace Engineering • Electrical Engineering • Voice detection • Radio

  24. Electrical engineering • Near perfect • Hemispherical • Circularly polarized

  25. What is it good for? • Medical/Biological • Chemistry • Programming • Robotics - Sports • Financial prediction • Data mining • Radar • Recognition

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