Finding a Sorting Algorithm Using Genetic Programming

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# Finding a Sorting Algorithm Using Genetic Programming - PowerPoint PPT Presentation

Finding a Sorting Algorithm Using Genetic Programming. May 22, 2003 Hyunkoo Jee. Defining a Virtual Machine and the Syntax of the Programming Language (1/4). Resources that can be used by the VM Array of registers : A[0] , … , A[N-1] Index registers : I1 , I2 (initial value : 0)

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## Finding a Sorting Algorithm Using Genetic Programming

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1. Finding a Sorting AlgorithmUsing Genetic Programming May 22, 2003 Hyunkoo Jee

2. Defining a Virtual Machine and the Syntax of the Programming Language (1/4) • Resources that can be used by the VM • Array of registers : A[0], …, A[N-1] • Index registers : I1, I2 (initial value : 0) • Machine Instructions and the Syntax • Syntax • One instruction (and its operands) per one line • An instruction can have at most two operands • Line number is assigned as 0, 1, 2, …

3. Defining a Virtual Machine and the Syntax of the Programming Language (2/4) • Example 0: PUT I2 ← I1 1: INC I2 2: EXCH 3: BR (I2 >= N-1) 5 4: BR (I1 < I2) 1 5: INC I1 6: BR (I1 < I2) 0

4. Defining a Virtual Machine and the Syntax of the Programming Language (3/4) • Instructions • EXCH : (Exchange) First, it compares A[I1] and A[I2]. And, if there is an inversion, it exchanges the values of A[I1] and A[I2]. If “0<=I1<=N-1 and 0<=I2<=N-1” is not satisfied, this instruction doesn’t do anything. • INC operand1 : (Increase) It increases the value of the register specified in operand1. Operand1 can be I1 or I2. • DEC operand1 : (Decrease) It decreases the value of the register specified in operand1. Operand1 can be I1 or I2. • PUT operand1 : It copies the value of a register into another register. Operand1 can be “I2 ← I1” or “I1 ← I2”. • BR operand1 operand2 : (Conditional branch) If the condition specified in operand1 is true, jump to the line specified in operand2. operand1 can be… • I1 <= 0 • I2 <= 0 • I1 >= N-1 • I2 >= N-1 • I1 < I2 • I1 > I2 • I1 = I2 operand2 is the line number to jump to.

5. Defining a Virtual Machine and the Syntax of the Programming Language (4/4) • A priori constraints that the programs must satisfy • Use the instruction “EXCH” exactly one time

6. Defining the Cost Function (or Fitness Function) • Weighted sum of • Inversion = #( {(i,j) | i < j and A[i] > A[j], 0<=i,j<=N-1} ) • Runtime = (how many instructions executed) • Length = (how many lines)

7. Evaluation • Implement the virtual machine, or an emulating program • Execute the individual candidates(=programs) on this VM, and compute the cost function of each candidates. • Here we generate T arrays, each array has N elements. Let each individual sort these T arrays. And we compute the average of the T cost values of each individual. • If “runtime” exceed some threshold, stop the VM (unless it would run an infinite loop).

8. Defining the Evolutionary Operations • 4 Mutation Methods • Insert a new line • When inserting a line including “BR”, the “operand2” should be in an adequate range. • Don’t insert “EXCH” line. • Delete a line • Don’t delete “EXCH” line. • Swap two lines • Mutate a line • Do not touch the instruction. Mutate the operands only. • To mutate an individual candidate, randomly apply these 4 methods, O times.

9. Selection(how to generate the next generation)

10. Summary of the Procedure • Make P initial individual candidates • At first, each individual(=program) has only one line, i.e. “EXCH” • To generate the initial population, apply the mutation methods I times to each individual. • Evaluate the cost function • Generate T arrays, each of them has N elements. • Put these T arrays to each individual in current generation. And evaluate the cost function f for each individual. • Selection • Classify the individuals in the current generation as : best group(S candidates), worst group(S), ordinary group(P-2S)(according to the cost-function ranking) • Make the next generation with : best group, mutated best group, mutated ordinary group(Here we apply the mutation method O times, in order to mutate an individual) • Jump to “Evaluate the cost function” again, and repeat

11. Experimental Condition • T = 20 • How many sample arrays, needed to evaluate the cost function of a candidate? • N = 10 • How many elements in each array? • I = 20 • How many times we apply the mutation methods, when we generate the initial population? • O = 5 • How many times we apply the mutation methods, when we mutate an individual? • Wruntime = 0.0005 • Winversion = 0.9945 • Wlength = 0.005 • P = 500 • How many individuals in a generation? • S = 120 • How many individuals in the best group (preserved for the next generation)? = How many individuals in the worst group?

12. Experimental Results

13. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

14. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

15. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

16. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177 I1과 I2를 N-1 까지 증가시키는 루프

17. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

18. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177 I1을 계속 감소시키면서, A[I1]과 A[I2]가 inversion된 경우 이 둘을 exchange 결국 A[0]~A[I2] 중 가장 큰 값이 A[I2]로 들어가게 된다

19. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

20. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

21. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

22. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

23. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177 I1을 계속 감소시키면서, A[I1]과 A[I2]가 inversion된 경우 이 둘을 exchange 결국 A[0]~A[I2] 중 가장 큰 값이 A[I2]로 들어가게 된다

24. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

25. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

26. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

27. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

28. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177 I1을 계속 감소시키면서, A[I1]과 A[I2]가 inversion된 경우 이 둘을 exchange 결국 A[0]~A[I2] 중 가장 큰 값이 A[I2]로 들어가게 된다

29. Experimental Results • After the 2000th generation, the best individual doesn't change. • The best individual 0: DEC I2 1: BR (I2 <= 0) 12 2: PUT I1 ← I2 3: DEC I1 4: EXCH 5: BR (I1 <= 0) 0 6: DEC I1 7: BR (I1 < I2) 4 8: INC I1 9: PUT I2 ← I1 10: BR (I1 >= N-1) 3 11: INC I1 12: BR (I1 > I2) 8 • Runtime = 224, Inversion = 0, Length = 13 • Cost Function = 0.177

30. Analysis of the Results • selection sort (putting the correct value from right to left) • Time complexity : O(N2) • Not the optimal solution, but not so bad • Diversity preserved • It is important to define the virtual machine and its instructions adequately • Future works • Using crossover • Using simulated annealing • Using general x86 machine instruction • modular structure (make sub-routines and call them) • Recursive call → O(N logN) algorithm • Generalization → apply to other problems

31. Conclusion • 주어진 문제를 해결하는 알고리즘을 찾기 위하여, EA(Evolutionary Algorithm)를 이용하여 컴퓨터 프로그램을 진화시키는 전략 (Genetic Programming) • 적절한 알고리즘을 찾아내기 어려운 문제가 주어진 경우, 문제에 어느 정도 접근한 간단한 프로그램을 작성한 뒤에, 이 프로그램을 진화시켜 볼 수 있다. 그러면 문제를 더욱 잘 푸는 새로운 알고리즘을 찾을 수 있을 것이다. • 다항 시간 (polynomail time) 내에 푸는 알고리즘이 없는 NP class나 그 밖의 문제들을 풀기 위해선, 휴리스틱(heuristic) 방법을 사용하여 어느 정도 타당한 해를 구하는 방법을 많이 쓴다. 이때 사용하는 휴리스틱 알고리즘을 잘 선택하는 것이 성능에 큰 영향을 미친다. 이런 경우 역시 EA를 이용하여 더 좋은 휴리스틱 알고리즘을 찾아낼 수 있을 것이다. • 최적화(optimization)가 아닌 다양성(diversity)을 목적으로 EA를 사용한다면, 기대하지 못했던 새로운 기능을 하는 프로그램이 창발(emergence)할 수도 있을 것이다.