152,847 research outputs found
A hybrid approach to protein folding problem integrating constraint programming with local search
<p>Abstract</p> <p>Background</p> <p>The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.</p> <p>Results</p> <p>Using the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach.</p> <p>Conclusion</p> <p>Constraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.</p
Value withdrawal explanations: a theoretical tool for programming environments
Constraint logic programming combines declarativity and efficiency thanks to
constraint solvers implemented for specific domains. Value withdrawal
explanations have been efficiently used in several constraints programming
environments but there does not exist any formalization of them. This paper is
an attempt to fill this lack. Furthermore, we hope that this theoretical tool
could help to validate some programming environments. A value withdrawal
explanation is a tree describing the withdrawal of a value during a domain
reduction by local consistency notions and labeling. Domain reduction is
formalized by a search tree using two kinds of operators: operators for local
consistency notions and operators for labeling. These operators are defined by
sets of rules. Proof trees are built with respect to these rules. For each
removed value, there exists such a proof tree which is the withdrawal
explanation of this value.Comment: 14 pages; Alexandre Tessier, editor; WLPE 2002,
http://xxx.lanl.gov/abs/cs.SE/020705
A Constraint-directed Local Search Approach to Nurse Rostering Problems
In this paper, we investigate the hybridization of constraint programming and
local search techniques within a large neighbourhood search scheme for solving
highly constrained nurse rostering problems. As identified by the research, a
crucial part of the large neighbourhood search is the selection of the fragment
(neighbourhood, i.e. the set of variables), to be relaxed and re-optimized
iteratively. The success of the large neighbourhood search depends on the
adequacy of this identified neighbourhood with regard to the problematic part
of the solution assignment and the choice of the neighbourhood size. We
investigate three strategies to choose the fragment of different sizes within
the large neighbourhood search scheme. The first two strategies are tailored
concerning the problem properties. The third strategy is more general, using
the information of the cost from the soft constraint violations and their
propagation as the indicator to choose the variables added into the fragment.
The three strategies are analyzed and compared upon a benchmark nurse rostering
problem. Promising results demonstrate the possibility of future work in the
hybrid approach
Mining local staircase patterns in noisy data
Most traditional biclustering algorithms identify biclusters with no or little overlap. In this paper, we introduce the problem of identifying staircases of biclusters. Such staircases may be indicative for causal relationships between columns and can not easily be identified by existing biclustering algorithms. Our formalization relies on a scoring function based on the Minimum Description Length principle. Furthermore, we propose a first algorithm for identifying staircase biclusters, based on a combination of local search and constraint programming. Experiments show that the approach is promising
Encapsulated search and constraint programming in Oz
Oz is an attempt to create a high-level concurrent programming language providing the problem solving capabilities of logic programming (i.e., constraints and search). Its computation model can be seen as a rather radical extension of the concurrent constraint model providing for higher-order programming, deep guards, state, and encapsulated search. This paper focuses on the most recent extension, a higher-order combinator providing for encapsulated search. The search combinator spawns a local computation space and resolves remaining choices by returning the alternatives as first-class citizens. The search combinator allows to program different search strategies, including depth-first, indeterministic one solution, demand-driven multiple solution, all solutions, and best solution (branch and bound) search. The paper also discusses the semantics of integer and finite domain constraints in a deep guard computation model
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