1. Counting evolutionary changes the parsimony method requires an algorithm that counts the number of evolutionary changes in a tree. Fitch W.M. 1971. Syst. Zool. 20: 406-416.

2. the Fitch algorithm counts the number of changes in a tree with nucleotide sequence data (A, C, T, G) Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Fitch algorithm Walter M. Fitch

3. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Fitch algorithm [C][A][C][A][G] for a given site, note the bases observed in the tip species.

4. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Fitch algorithm [C][A][C][A][G] [AG] [AC] [ACG] [AC] intersection is empty; create union, count+1. intersection is empty; create union, count+1. intersection not empty; note intersection. intersection is empty; create union, count+1. the number of changes equals the number of empty intersections.

5. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Fitch algorithm is not required for 1.invariant sites (AAAAAAA) 2.sites with a single variant base (AATAAAA) 3.sites with similar patterns (TCTCA = CACAG)

6. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 0 C 0 G 0 T 0 in the Sankoff algorithm, not all changes are equally likely. David Sankoff

7. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 01 C 01 G 10 T 10 pyrimidine purine pyrimidine purine pyrimidine purine pyrimidine purine the cost of a transition is 1

8. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] the cost of a transversion is 2.5. pyrimidine purine pyrimidine purine pyrimidine purine pyrimidine purine

9. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] ACGT A  C + A  A =2.5 + 0 =2.5

10. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5

11. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5 1515

12. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5 1515 A  C =2.5 A  A=0

13. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5 1515 C  C =0 C  A=2.5

14. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5 1515 G  C =2.5 G  G=0

15. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5 1515 4.5 T  C =1 T  A=2.5

16. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5 1515 4.5 6 A  A =0 A  A =0

17. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm ACGT A 02.51 C 0 1 G 1 0 T 1 0 [C][A][C][A][G] 2.5 3.5 1515 4.5 6678 T  T =0 T  C =1 minimal cost of tree

18. Fitch W.M. 1971. Syst. Zool. 20: 406-416. The Sankoff algorithm the Sankoff algorithm is an example of a dynamic programming algorithm – it solves a large problem by first solving some smaller problems. David Sankoff