Genetic Algorithm Approach Prisoners,Guards Puzzle

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Genetic Algorithm Approach Prisoners, Guards Puzzle, CPSC 5185 Intro Artificial Intelligence, Finding Maximum No. Prisoners, Characterize p(n), max. no. prisoners valid n? board.

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A Genetic Algorithm Approach to the Prisoners & Guards Puzzle : A Genetic Algorithm Approach to the Prisoners & Guards Puzzle CPSC 5185 Intro to Artificial Intelligence April 6, 2004

Description of Prisoners & Guards Puzzle : Description of Prisoners & Guards Puzzle

Adjacent Squares : Adjacent Squares

Sample 3x3 Board : Sample 3x3 Board 5 prisoners – can we do better?

A Better 3x3 Board : A Better 3x3 Board 6 prisoners – this is the maximum

Some 4x4 Boards : Some 4x4 Boards

An Optimal 4x4 Board : An Optimal 4x4 Board

Finding Maximum No. Prisoners : Finding Maximum No. Prisoners Characterize p(n), the max. no. prisoners on a valid n n board.

Larger Values of n : Larger Values of n Brute force doesn’t work Trying an evolutionary approach

Genetic Algorithm Overview : Genetic Algorithm Overview Each generation has 500 chromosomes Spawn offspring through crossover and mutation operations 100 most fit in a generation retained in next generation

GA Overview, continued : GA Overview, continued Parents selected randomly, selection probabilities are proportional to fitness vals Two parents always produce two offspring Cataclysm prevents stagnation

Converting Boards to Chromosomes : Converting Boards to Chromosomes Treat each board configuration as a “chromosome” (ignore placement rule)

The Crossover Operation : The Crossover Operation Crossover probability 0.7. Parents Children

Mutation : Mutation Mutation probability 0.001 for each board square Toggle value if mutation occurs

Survival of the fittest : Survival of the fittest Fitness function 1: fitness = total number of prisoners Fitness function 2: fitness = (no. pris.) x (validity) validity = (# valid sqrs) / (total sqrs)

Fitness Comp. Example : Fitness Comp. Example F1 = 6 F2 = 6 ( 5 / 9 ) = 30 / 9

Outline of the Algorithm : Outline of the Algorithm See handout sheet

Comparing Performances Using Two Fitness Functions : Comparing Performances Using Two Fitness Functions See Excel printout

My Best 5x5 Example : My Best 5x5 Example

A 6x6 Example : A 6x6 Example

Lower Bounds for p(n) : Lower Bounds for p(n) For odd n, striping pattern yields For even n, goose pattern yields

A Better 6x6 : A Better 6x6

Striping Not Best for 9x9 : Striping Not Best for 9x9

Questions : Questions Is there a better lower bound for p(n)? What is a good upper bound for p(n)?

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