Abstract
The problem of inferring ancestral genetic information in terms of a set of founders of a given population arises in various biological contexts. In optimization terms, this problem can be formulated as a combinatorial string problem. The main problem of existing techniques, both exact and heuristic, is that their time complexity scales exponentially, which makes them impractical for solving large-scale instances. Basing our work on previous ideas outlined in [1], we developed a randomized iterated greedy algorithm that is able to provide good solutions in a short time span. The experimental evaluation shows that our algorithm is currently the best approximate technique, especially when large problem instances are concerned.
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Benedettini, S., Blum, C., Roli, A. (2010). A Randomized Iterated Greedy Algorithm for the Founder Sequence Reconstruction Problem. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_4
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DOI: https://doi.org/10.1007/978-3-642-13800-3_4
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