rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Metaheuristics set
    part number: Volume 6
    part name: Metaheuristics for string problems in bio-informatics
  title info:
    title: Metaheuristics for string problems in bio-informatics
  name:
    name part: Blum, C. (Christian)
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Festa, Paola
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 volume) : illustrations.
  abstract: This book will present the latest research on metaheuristic algorithms for some of the most important string problems in bio-informatics. Optimization problems related to strings-such as protein or DNA sequences-are very common in bioinformatics. Examples include string selection problems such as the "far from most string" problem, and other string problems such as the longest common subsequence problem and its variants, alignment problems, and similarity search. These problems are often computationally very hard. Therefore, during the last 10-15 years the research community has focused especially on metaheuristic algorithms for solving this type of problems. This book aims at presenting some of the most interesting recent work in this line of research.
  table of contents: Cover ; Dedication; Title Page ; Copyright ; Contents; Preface; Acknowledgments; List of Acronyms; 1. Introduction; 1.1. Complete methods for combinatorial optimization; 1.1.1. Linear programming relaxation; 1.1.2. Cutting plane techniques; 1.1.3. General-purpose ILP solvers; 1.1.4. Dynamic programming; 1.2. Approximate methods: metaheuristics; 1.2.1. Ant colony optimization; 1.2.2. Evolutionary algorithms; 1.2.3. Greedy randomized adaptive search procedures; 1.2.4. Iterated local search; 1.2.5. Simulated annealing; 1.2.6. Other metaheuristics; 1.2.7. Hybrid approaches.
  table of contents: 1.3. Outline of the book2. Minimum Common String Partition Problem; 2.1. The MCSP problem; 2.1.1. Technical description of the UMCSP problem; 2.1.2. Literature review; 2.1.3. Organization of this chapter; 2.2. An ILP model for the UMCSP problem; 2.3. Greedy approach; 2.4. Construct, merge, solve and adapt; 2.5. Experimental evaluation; 2.5.1. Benchmarks; 2.5.2. Tuning CMSA; 2.5.3. Results; 2.6. Future work; 3. Longest Common Subsequence Problems; 3.1. Introduction; 3.1.1. LCS problems; 3.1.2. ILP models for LCS and RFLCS problems; 3.1.3. Organization of this chapter.
  table of contents: 3.2. Algorithms for the LCS problem 3.2.1. Beam search; 3.2.2. Upper bound; 3.2.3. Beam search framework; 3.2.4. Beam-ACO; 3.2.5. Experimental evaluation; 3.3. Algorithms for the RFLCS problem; 3.3.1. CMSA; 3.3.2. Experimental evaluation; 3.4. Future work; 4. The Most Strings With Few Bad Columns Problem; 4.1. The MSFBC problem; 4.1.1. Literature review; 4.2. An ILP model for the MSFBC problem; 4.3. Heuristic approaches; 4.3.1. Frequency-based greedy; 4.3.2. Truncated pilot method; 4.4. ILP-based large neighborhood search; 4.5. Experimental evaluation; 4.5.1. Benchmarks; 4.5.2. Tuning of LNS.
  table of contents: 4.5.3. Results4.6. Future work; 5. Consensus String Problems; 5.1. Introduction; 5.1.1. Creating diagnostic probes for bacterial infections; 5.1.2. Primer design; 5.1.3. Discovering potential drug targets; 5.1.4. Motif search; 5.2. Organization of this chapter; 5.3. The closest string problem and the close to most string problem; 5.3.1. ILP models for the CSP and the CTMSP; 5.3.2. Literature review; 5.3.3. Exact approaches for the CSP; 5.3.4. Approximation algorithms for the CSP; 5.3.5. Heuristics and metaheuristics for the CSP.
  table of contents: 5.4. The farthest string problem and the far from most string problem5.4.1. ILP models for the FSP and the FFMSP; 5.4.2. Literature review; 5.4.3. Heuristics and metaheuristics for the FFMSP; 5.5. An ILP-based heuristic; 5.6. Future work; 6. Alignment Problems; 6.1. Introduction; 6.1.1. Organization of this chapter; 6.2. The pairwise alignment problem; 6.2.1. Smith and Waterman's algorithm; 6.3. The multiple alignment problem; 6.3.1. Heuristics for the multiple alignment problem; 6.3.2. Metaheuristics for the multiple alignment problem; 6.4. Conclusion and future work; 7. Conclusions.
  note: Christian Blum, Paola Festa.
  note: Includes bibliographical references and index.
  subject:
    topic: Bioinformatics
  subject:
    topic: Heuristic algorithms
  subject:
    topic: Computational Biology
  classification: QH324.2 .B58 2016
  classification: 572.80285
  related item:
    title info:
      title: Metaheuristics set. Volume 6, Metaheuristics for string problems in bio-informatics
    name:
      name part: Blum, C. (Christian).
    origin info:
      publisher: London, UK : ISTE Ltd ; Hoboken, NJ : John Wiley & Sons, 2016
  related item:
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  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 170116
    record change date: 20260214205143.4
    record identifier: in00004683611
    language of cataloging:
      language term: eng