1. Optimizing the State Eval Heuristic of Abalone using Evolutionary Algorithms Benjamin Rhew November 28, 2005

2. The Roadmap Why Should We Optimize? Why Use an Evolutionary Algorithm? Previous Work Abalone Explained Experimental Setup What is a Two-Pool EA? Evolutionary Algorithm Details Future Work

3. Why Should We Optimize? Heuristics are everywhere Spam filters Speech recognition software Have limitations Slow, but good solution Fast, but poor solution So optimize!

4. Why Use an Evolutionary Algorithm? Heuristic development takes time Improbable for a person to design the optimal heuristic Need to test many different heuristics Therefore, an EA is called for

5. Previous Work There is a lot of previous work, mainly involving chess heuristics Chess-specific algorithms Works mainly on chess playing programs Do not play among themselves Population dynamics This approach should be more general

6. Abalone Explained Board Game developed in the 1990s Sumo Wrestling with Marbles... Program Demo

7. Experimental Setup Main Idea – the fitness of an individual depends on how well that individual plays games Randomly chosen Play 2 games, one as each color Takes a long time Therefore, a steady-state two-pool EA with high selective pressure is used

8. What is a Two-Pool EA? An EA where individuals are in two groups Can use many separations Male/Female Predator/Prey Child/Adult In this case, child/adult is used

9. Evolutionary Algorithm Details Parent Selection – stochastic, based on fitness Survival Selection – stochastic/elitist, with worst individual having highest chance of dying Initial Population – small, 20 individuals Recombination – N-point crossover Mutation – Gaussian Random Variable Fitness – number of wins divided by the number of games played Initialization – small random floats near 1

10. Evolutionary Algorithm Details Evolutionary Process – individual will not be killable in the first 10 generations of its life Three possible Gene Representations: Take each part of the previous heuristic and multiply it by a constant Subdivide parts of the previous heuristic and multiply each part by a constant Completely subdivide previous heuristic and multiply each part by a constant

11. Evolutionary Algorithm Details Gene 1: board1, board2, mymarbles, oppmarbles, age Gene 2: board1_1...board1_5, board2_1...board2_5, mymarbles, oppmarbles, age Gene 3: board1_1_1...board1_9_9, board2_1_1...board2_9_9, mymarbles, oppmarbles, age

12. Evolutionary Algorithm Details Gene 1: Gene 2: Gene 3:

13. Future Work Results Compare different fitness function strategies Compare different gene representations Use Genetic Programming to evolve new heuristics from scratch

14. Questions?