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.
Highlights
In recent years, more and more biologically inspired problems have been studied by operational researchers
We have proposed a unified encoding for the hyper-heuristic approach, which should provide an easier way to address different combinatorial problems
We introduced more generalization by employing the unified encoding and using a set of low level heuristics which are specific to that encoding rather than to the particular problem domain
Summary
More and more biologically inspired problems have been studied by operational researchers. This paper presents a unique insight into the DNA sequencing by hybridization problem from the perspective of developing generic search techniques which could be applied to different combinatorial optimization problems. Hyper-heuristic approaches can adapt themselves to the problem at hand, operating on the supplied low level heuristics set, yet giving satisfactory results with less “problem tailoring” required (Burke et al 2003b). We propose a unified encoding for the selected optimization problems to be used in the hyper-heuristic framework. A good performance of the hyper-heuristic will open the door to the investigation of their applicability to other combinatorial optimization problems including bioinformatics ones, which could be represented by the proposed unified encoding.
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