1. Research Trends in AI Maze Solving using GA Muhammad Younas 2005-02-0110 Hassan Javaid 2005-02-0304 Danish Hussain 2006-02-0225

2. Flash Back In the last presentation we showed our implementation of two functions. One of them was the mutate function and the other was the cross over function. In this presentation we have identified many other functions that we will be needing for project and we have made the pseudo code of many of them and have implemented some of them.

3. Functions 1. Mutate Takes a genome, returns the same genome with some of the bits flipped. 2. Cross Over Takes 2 genomes, performs one-point crossover on them to produce two new genomes. 3. Mate Takes 2 individuals and performs crossover on their genomes to get 2 new genomes. It then mutates the new genomes. Finally, it makes 2 new individuals with dummy values for fitness and phenome.

4. Functions continued Selecting an Individual Takes a population of individuals. Chooses a single individual randomly and returns that individual. This random choice is based on the fitness value of a genome in the population Random Population Takes a population size and number of genes in each genome and generates a population of individuals with random genomes Random Genome This function takes a genome-length and returns a random genome of that length.

5. Functions continued Make and Access individual's components This function will takes a fitness, genome and phenome and returns a list containing these items. Run Genetic Algorithm It creates an initial population with random-pop and then does the following things for each generation: * Creates the phenome for each individual in the population. * Evaluates the fitness of each individual. * Selects individuals and mates them to produce the new generation

6. Simulation Plan Simulations would be run on a variety of mazes to ensure the reliability of the algorithm Fitness value would be determined by the number of steps taken in the maze This fitness value could be changed to obtain the optimal results in the simulation

7. Testing Genetic Algorithm It will be good to make sure that there are enough genes to at least allow the robot to finish the maze. We have researched that it is always useful to have large populations. How much it is useful we will see once we have implemented the whole GA.

10. Reference Information Please refer to our previous presentation for a tentative GUI The Mutate and Crossover function are also present in our previous presentation Simulations results would be presented next week when implementation is complete

11. Q & A