Unified encoding for hyper-heuristics with application to bioinformatics

Aleksandra Swiercz, Edmund Burke, Mateusz Cichenski (), Grzegorz Pawlak, Sanja Petrovic, Tomasz Zurkowski and Jacek Blazewicz

Central European Journal of Operations Research, 2014, vol. 22, issue 3, 567-589

Abstract: This paper introduces a new approach to applying hyper-heuristic algorithms to solve combinatorial problems with less effort, taking into account the modelling and algorithm construction process. We propose a unified encoding of a solution and a set of low level heuristics which are domain-independent and which change the solution itself. This approach enables us to address NP-hard problems and generate good approximate solutions in a reasonable time without a large amount of additional work required to tailor search methodologies for the problem in hand. In particular, we focused on solving DNA sequencing by hybrydization with errors, which is known to be strongly NP-hard. The approach was extensively tested by solving multiple instances of well-known combinatorial problems and compared with results generated by meta heuristics that have been tailored for specific problem domains. Copyright The Author(s) 2014

Keywords: Bioinformatics; Hyper-heuristics; Simulated annealing; Choice function; Combinatorial problems; Sequencing by hybrydization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10100-013-0321-8

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