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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Inference of a Phylogenetic Tree: Hierarchical Clustering versus Genetic Algorithm
verfasst von : Glenn Blanchette, Richard O’Keefe, Lubica Benuskova
Erschienen in: AI 2012: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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This paper compares the implementations and performance of two computational methods, hierarchical clustering and a genetic algorithm, for inference of phylogenetic trees in the context of the artificial organism
Caminalcules
. Although these techniques have a superficial similarity, in that they both use agglomeration as their construction method, their origin and approaches are antithetical. For a small problem space of the original species proposed by Camin (1965) the genetic algorithm was able to produce a solution which had a lower Fitch cost and was closer to the theoretical evolution of
Caminalcules.
Unfortunately for larger problem sizes its time cost increased exponentially making the greedy directed search of the agglomerative clustering algorithm a more efficient approach.