Applications of Genetic Algorithms TJHSST Computer Systems Lab By Mary Linnell

What is a Genetic Algorithm? Evolutionary algorithm Population consisting of individuals Needs a function to evaluate each individual Least fit individuals killed off Best fit individuals breed with rest of population /10/king_penguin_breeding_1sfw.jpg A population of penguins

Genetic Algorithm Applications N Queens Problem Optimizing of Traveling Salesman Problem Othello Any problem with a population able to be characterized by a function

Purpose and Goals Find minimum point of a three-dimensional graph Testing every point would involve too many computations Use genetic algorithms to simplify this problem

Purpose and Goals Vary the population size to see what is “best” If too small  Population not representative of search space  Population will converge to a local minimum  Too many random mutations to find true solution If too large  Long run times  Large amount of computer space and memory

Scope of Study and Project C  Genetic algorithm  Analytical display of results OpenGL  3D graphics  Visual display of results Approximate answer should closely match exact answer

Procedure and Methods Wire-mesh display of graph with mouse controls Lots of local minimums N randomly-generated yellow points, where N is the number in the population

Controls Reset: re-initializes all values, increments seed Step: steps through the genetic algorithm One iteration: runs 5 steps (a full cycle) Single Trial: runs a fixed number of iterations Multiple Trials: runs a fixed number of single trials

Procedure and Methods Cycle of the genetic algorithm steps:  25% of the population population selected based on the fitness function  Delete selected points  New points are bred based on best point  Random mutation  New points become part of the population

Results of a Single Trial Should approximate exact answer Shown visually Shown numerically

Results of Multiple Trials True z-value for graph used: Population size of 8  Average result:  Difference from true minimum:

Future Testing Change other parameters of genetic algorithm Change the equation of the graph See changes in performance and number of iterations