1. Robot Intelligence Technology Lab. Generalized game of life 2003. 05. 22 20025059 YongDuk Kim

2. 2 Robot Intelligence Technology Lab. 0. Content Introduction Original game of life Create generalized game of life Using GA Result Conclusion

3. 3 Robot Intelligence Technology Lab. 1. Introduction Goal Implement original game of life system Make this system more general This is achieved by using Genetic Algorithm.

4. 4 Robot Intelligence Technology Lab. 2. Original Game of Life Rule If the number of surrounding cells is less than 2 or greater than 3, the current cell dies. If the number of living cells is exactly 2, or if the number of living cells is 3, maintain status. If the current cell is dead, but has three living cells surrounding it, it will come to life. Implementation 40 X 40 cells Each bound is connected Using C++

5. 5 Robot Intelligence Technology Lab. 3. Create Generalized game of life using GA GA is used to make generalized game of life. Procedure GA Begin t ← 0 Initialize P(t) Evaluate P(t) While (not termination-condition) do Begin t ←t+1 Select P(t) from P(t-1) Alter P(t) Evalutate P(t) End end

6. 6 Robot Intelligence Technology Lab. 3. Create Generalized game of life using GA Components of GA A genetic representation A way to create an initial population Selection method An evaluation function Genetic operators

7. 7 Robot Intelligence Technology Lab. 3. Create Generalized game of life using GA A genetic representation A way to create initial population Randomly Current cell is dead and has no neighbor. Current cell is dead and has “ 1 ” neighbors. Current cell is alive and has no neighbor. Current cell is alive and has “ 1 ” neighbor. Current cell is alive and has “ 8 ” neighbors.

8. 8 Robot Intelligence Technology Lab. 3. Create Generalized game of life using GA Selection method Roulette wheel method  Probability of selection Evaluation function Best solution has maximum fitness. My interest world  Active  Not crowded

9. 9 Robot Intelligence Technology Lab. 3. Create Generalized game of life using GA How? Active  Look at 2 generations in the Game of life, then check how many cells are different.  Let’s call this fitness1. Not crowded  Find a deviation from my desired number of cells.  Then invert this deviation, because we want to maximize fitness.  Let’s call this fitness2. fitness = W1 * fitness1 + W2 * fitness2

10. 10 Robot Intelligence Technology Lab. 3. Create Generalized game of life using GA We must look the global feature Each generation of GA is consisted of 500 generation of Game of Life Average this fitnesses. Final Fitness

11. 11 Robot Intelligence Technology Lab. 3. Create Generalized game of life using GA Genetic operator Mutation  With probability Pm Crossover  With probability Pc Elitist  Keep the best solution!

12. 12 Robot Intelligence Technology Lab. Result Number of Runs: 10 Population size: 100 Max generation: 200 P XOVER : 0.25 P MUTATION : 0.02

13. 13 Robot Intelligence Technology Lab. Result Result 1 I want half of the cells is alive, and active world. I think First constraints is 5 times important than second condition  W1 = 5.0  W2 = 1.0 001111111000000011 => Best fitness = 5599.918000000 for 200 generation 111111111000000000 => Best fitness = 5599.999990000 for 73 generation 001111111000000011 => Best fitness = 5599.940000000 for 200 generation 011111111000000001 => Best fitness = 5599.996000000 for 200 generation 011111111000000001 => Best fitness = 5599.994000000 for 200 generation 111111110100000000 => Best fitness = 5599.998000000 for 200 generation 001111111000000010 => Best fitness = 5599.878000000 for 200 generation 001111111000000010 => Best fitness = 5599.894000000 for 200 generation 111111110100000000 => Best fitness = 5599.984000000 for 200 generation 101111110000000010 => Best fitness = 5599.876000000 for 200 generation Many solution shows just inverting property.

14. 14 Robot Intelligence Technology Lab. Result Result 2 I want about 10 cells is alive, and active world. I think First constraints is twice important than second condition  W1 = 2.0  W2 = 1.0 000110100000000001 => Best fitness = 3021.632000000 for 200 generation 000110011000000000 => Best fitness = 3021.658000000 for 200 generation 000110011000000000 => Best fitness = 3021.666000000 for 200 generation ……………………..

15. 15 Robot Intelligence Technology Lab. Result Result 3 I want about 30 cells is alive, and active world. I think First and second constraints have same importance.  W1 = 1.0  W2 = 1.0 000101011010000111 => Best fitness = 1566.490000000 for 200 generation 000110011000001111 => Best fitness = 1581.482000000 for 200 generation 000100111010001111 => Best fitness = 1581.070000000 for 200 generation ……………………..

16. 16 Robot Intelligence Technology Lab. Conclusion Implementation the Game of Life using c++ Extend it to generalized Game of Life Using GA GA generates funny rules successfully, according to my intends. BUT, it is Somehow difficult to find the fitness function because it seems that this is a little subjective and vague.