Abstract
An evolutionary algorithm (EA) usually initializes its population with random genotypes, which represent random solutions to the target problem instance. If the problem is one of constrained optimization, an initial population whose genotypes all represent empty solutions might allow the EA to grow valid solutions as much as search for them and thereby identify good solutions more quickly. This is the case in a genetic algorithm (GA) for the longest common subsequence problem, which seeks the length of a longest subsequence common to each of a set of given strings. The GA encodes sequences as binary strings that indicate subsequences of the shortest or first given string. In tests on a variety of problem instances, the GA always identifies an optimum subsequence, but on most instances, the GA reaches an optimum more quickly when its initial population encodes empty sequences than when its initial genotypes represent random sequences.
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Julstrom, B.A., Hinkemeyer, B. (2006). Starting from Scratch: Growing Longest Common Subsequences with Evolution. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_94
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DOI: https://doi.org/10.1007/11844297_94
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