1. A Distributed Genetic Algorithm for Learning Evaluation Parameters in a Strategy Game
Gary Matthias

2. Objective
Design a genetic algorithm that learns the optimal set of evaluation parameters for the 5,5,4-game (Tic-Tac-Toe variant)
Implement a game simulation engine that uses an alpha-beta search to select moves
Parallelize the natural selection segment of the algorithm, i.e., game simulation
Compare parallel performance and serial performance

3. The 5,5,4-Game

4. Natural Selection Initial population of size n
Round-robin tournament: n*(n-1) games
Fitness/survival determined heuristically from game results:
5*wins + 1*draws - 4*losses
Non-surviving competitors replaced with children of surviving competitors
Chance of reproduction proportional to fitness score

5. Recombination
Mutation

6. Evaluation Parameters Used
Specific squares (e.g. corner vs. middle)
12 parameters, 8 bits each
Consecutive pieces (number and direction) (8 bits)
Scale factors (weight given to specific squares or consecutive pieces)
2 parameters, 8 bits each
Search depth
1 parameters, 3 bits each

7. Convergence
Convergence was not considered for this project, however a reasonable criterion for termination would be when all games end in draws for several consecutive rounds. (Beware local solutions!)
Since the 5,5,4-game is a theoretical draw with perfect play, a tournament of all draws may suggest that near-perfect strategy is being played by all competitors.

8. Parallel vs. Serial Performance
Performance determined by running time of n simulated matches of the 5,5,4-game
On starp.csail.mit.edu with 8 processes
Parallel vs. serial ratio decreases with increasing n
For a round-robin tournament of very large n, the parallel algorithm would significantly decrease running time
n = 8
n = 16
n = 32
Parallel
19.33 s
36.08 s
67.29 s
Serial
27.26 s
77.60 s s
Ratio
0.71
0.46
0.38