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The game

http://cs.gettysburg.edu/~cpresser/web3d/x3dom/MazeMockUp2.html

Genetic Algorithm

Description of the algorithm can be found here: https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3

Our implementation: To create a Genetic Algorithm for a problem.

  1. Create a subclass of GeneticChromosome.

  2. Create a subclass of ChromosomeFactory.

  3. Create a main program to run the simulation.

  4. Create a subclass of GeneticChromosome. Override the following abstract methods: public abstract int calculateFitness(); public abstract int length(); public abstract boolean getBit(int index); public abstract void setBit(int index, boolean value);

  5. Create a subclass of ChromosomeFactory. Override the following abstract methods: //create a default chromosome public abstract T createChromosome();

    //create a chromosome with random components set where the //parameter is the probability that a bit is set to 1 public abstract T createRandomChromosome(double probability);

    //override crossover to define different ways to handle the crossover operation // the default is random

  6. Create a main program to run the simulation. a. Construct a GeneticEvaluator object. b. Call the evolve method on that object.

My contribution:

Developed and impemented a new fitness evaluation function. The function generated a drastically different kind of puzzle. Further research direction is to find quantitative ways to compare the original and new kinds of puzzles. Advised by Dr. Clifton Pressor