Efficient Solutions for the Far From Most String Problem
Abstract: Computational molecular biology has emerged as one of the most exciting interdisciplinary fields. It has currently benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biochemistry, and computer science. In the past few years it has been shown that a large number of molecular biology problems can be formulated as combinatorial optimization problems,including sequence alignment problems, genome rearrangement problems, string selection and comparison problems, and protein structure prediction and recognition. This paper provides a detailed description of string selection and string comparison problems. For finding good-quality solutions of a particular class of string comparison molecular biology problems, known as the far from most string problem, we propose new heuristics, including a Greedy Randomized Adaptive Search Procedure (GRASP) and a Genetic Algorithm (GA). Computational results indicate that these randomized heuristics find better quality solutions compared with results produced by the best state-of-the-art heuristic approach.
Keywords: Computational biology, Molecular structure prediction, Protein and sequences alignment, Combinatorial optimization, Metaheuristics.
Category 1: Combinatorial Optimization
Category 2: Combinatorial Optimization (Meta Heuristics )
Category 3: Applications -- Science and Engineering (Basic Sciences Applications )
Citation: Technical Report, U. of Napoli Federico II, Napoli, Italy, 2008.
Entry Submitted: 04/07/2011
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