Research Trends in AI
Maze Solving using GA
Muhammad Younas 2005-02-0110
Hassan Javaid
2005-02-0304
Danish Hussain
2006-02-0225
Flash Back
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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.
Functions
1.
Mutate
2.
Cross Over
3.
Mate
Takes a genome, returns the same genome with some of the bits
flipped.
Takes 2 genomes, performs one-point crossover on them to produce
two new genomes.
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.
Functions continued
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Selecting an Individual
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Random Population
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
Takes a population size and number of genes in each genome and
generates a population of individuals with random genomes
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Random Genome
This function takes a genome-length and returns a random genome of
that length.
Functions continued
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Make and Access individual's components
This function will takes a fitness, genome and phenome and returns
a list containing these items.
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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
Simulation Plan
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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
Testing Genetic Algorithm
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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.
Reference Information
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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
Q&A