A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
EasyLocal++: an object-oriented framework for the flexible design of local-search algorithms
Software—Practice & Experience
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
An approximation algorithm for haplotype inference by maximum parsimony
Proceedings of the 2005 ACM symposium on Applied computing
Haplotype Phasing Using Semidefinite Programming
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Integer Programming Approaches to Haplotype Inference by Pure Parsimony
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
Efficient haplotype inference with boolean satisfiability
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Haplotype inference by pure Parsimony
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
Efficient haplotype inference with pseudo-boolean optimization
AB'07 Proceedings of the 2nd international conference on Algebraic biology
Symmetry breaking and local search spaces
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
SAT in bioinformatics: making the case with haplotype inference
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Two-Level ACO for Haplotype Inference Under Pure Parsimony
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
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Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods (Integer Linear Programming, Semidefinite Programming, SAT and pseudo-boolean encoding) that, at present, are adequate only for moderate size instances. In this paper, we present and discuss an approach based on the combination of local search metaheuristics and a reduction procedure based on an analysis of the problem structure. Some relevant design issues are first described, then a family of local search metaheuristics is defined to tackle the Haplotype Inference. Results on common Haplotype Inference benchmarks show that the approach achieves a good trade-off between solution quality and execution time.