Applications of Genetic Algorithms TJHSST Computer Systems Lab

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Presentation transcript:

Applications of Genetic Algorithms TJHSST Computer Systems Lab 2008-2009 By Mary Linnell

What is a Genetic Algorithm? Evolutionary algorithm Population consisting of individuals Least fit individuals killed off Best fit individuals bred with rest of population http://www.lifeinthefastlane.ca/wp-content/uploads/ 2007/10/king_penguin_breeding_1sfw.jpg A population of penguins

Genetic Algorithm Applications Othello AI N Queens Problem Optimizing of Traveling Salesman Problem Many other problems http://images.boardgamegeek.com/images/pic158681_md.jpg http://en.wikipedia.org/wiki/N_queens_problem

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

Procedure and Methods N randomly-generated yellow points, where N is the population size

Procedure and Methods Lots of local minimums Side view of graph

Original setup

25% of population selected

Selected individuals removed

New individuals bred

New individuals become part of the population

Random mutation

After a single trial...

Found local minimums

After a lot of iterations, random mutation helps

Results of Multiple Trials Population size 8 16 32 64 True z-value -0.84411 Average result -0.52009 -0.62066 -0.71237 -0.70805 Difference 0.32403 0.22345 0.13174 0.13607

Results of Multiple Trials

Future Study Improved algorithm to avoid local minima Change other parameters of genetic algorithm