Genetic Algorithms and TSP by fop21123

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									Genetic Algorithms and TSP




Thomas Jefferson Computer Research Project
             by Karl Leswing
Genetic Algorithms

   Effective in Optimization Problems
   Classified as a global search heuristic
   Inspired by Evolutionary Biology
       Inheritance
       Mutation
       Selection
       Crossover
Traveling Salesman

   Given a number of
    cities and the costs of
    traveling from any city
    to any other city, what
    is the cheapest round-
    trip rout that visits each
    city exactly once and
    then returns to the
    starting city.
Traveling Salesman Continued

   O(N!)‫‏‬
   Dynamic Programming down to O((n^2)*2^n)‫‏‬
   Find Near Optimal Solutions
Current Work
Current Work Continued

   Double Point Crossover
   Roulette Selection
       Unique Fitness Algorithm
   Single Point Mutation
       Mutation Rate Variable
   Effectiveness
       Solve 50 City TSP in less than one minute
Ant Colony Optimization

   Pheromone Trail
   Evaporation Rate
   Effective for dynamically changing graphs
   Useful for network routing and urban
    transportation systems
3 Dimensions

   Attempted Open GL with Pipe
   Attempted Jogl
       Java Bindings for OpenGl
   Given Up
   Work on Comparison of global search hueristics
Extensions

   Finish my ant colony
    optimization (ACO)‫‏‬
   Brute Force Automated
    Test
   Particle Swarm
    Optimization (PSO)‫‏‬
   Neural Networking

								
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