Two Algorithms for the Longest Common Subsequence of Three (or More) Strings
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
Analysis of evolutionary algorithms for the longest common subsequence problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Simulated annealing, its parameter settings and the longest common subsequence problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Starting from scratch: growing longest common subsequences with evolution
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Computing longest common subsequences with the B-cell algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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A genetic algorithm for the longest common subsequence problem encodes candidate sequences as binary strings that indicate subsequences of the shortest or first string. Its fitness function penalizes sequences not found in all the strings. In tests on 84 sets of three strings, a dynamic programming algorithm returns optimum solutions quickly on smaller instances and increasingly slowly on larger instances. Repeated trials of the GA always identify optimum subsequences, and it runs in reasonable times even on the largest instances.