JCP 2013 Vol.8(11): 2934-2941 ISSN: 1796-203X
doi: 10.4304/jcp.8.11.2934-2941
doi: 10.4304/jcp.8.11.2934-2941
A Parthenogenetic Algorithm for the Founder Sequence Reconstruction Problem
Jingli Wu and Hua Wang
College of Computer Science and Information Technology, Guangxi Normal University, Guilin, 541004, China
Abstract—The maximum fragment length (MFL) is an important computational model for solving the founder sequence reconstruction problem. Benedettini et al. presented a meta-heuristic algorithm BACKFORTH based on iterative greedy method. The BACKFORTH algorithm starts with a single initial solution, and iteratively alternates between a partial destruction and reconstruction in order to obtain a final solution. The kind of optimization mechanism, which is based on a single initial solution, may make the performance of the BACKFORTH algorithm sensitive to the quality of the initialization. In this paper, a practical parthenogenetic algorithm PGMFL, which is a populationbased meta-heuristic method, is proposed. The PGMFL algorithm can search multiple regions of a solution space simultaneously. A novel genetic operator is introduced based on the presented heuristic algorithm HF, which takes advantage of look-ahead mechanism and some potential information, i.e., the proportions of 0 and 1 entries in a column of recombinant matrix and those in the corresponding column of the founder matrix, and some other heuristic information, to compute the column values. The PGMFL algorithm can get fewer breakpoints and longer fragment average length than the BACKFORTH algorithm, which are proved by a number of experiments.
Index Terms—founder, reconstruction, parthenogenetic algorithm
Abstract—The maximum fragment length (MFL) is an important computational model for solving the founder sequence reconstruction problem. Benedettini et al. presented a meta-heuristic algorithm BACKFORTH based on iterative greedy method. The BACKFORTH algorithm starts with a single initial solution, and iteratively alternates between a partial destruction and reconstruction in order to obtain a final solution. The kind of optimization mechanism, which is based on a single initial solution, may make the performance of the BACKFORTH algorithm sensitive to the quality of the initialization. In this paper, a practical parthenogenetic algorithm PGMFL, which is a populationbased meta-heuristic method, is proposed. The PGMFL algorithm can search multiple regions of a solution space simultaneously. A novel genetic operator is introduced based on the presented heuristic algorithm HF, which takes advantage of look-ahead mechanism and some potential information, i.e., the proportions of 0 and 1 entries in a column of recombinant matrix and those in the corresponding column of the founder matrix, and some other heuristic information, to compute the column values. The PGMFL algorithm can get fewer breakpoints and longer fragment average length than the BACKFORTH algorithm, which are proved by a number of experiments.
Index Terms—founder, reconstruction, parthenogenetic algorithm
Cite: Jingli Wu and Hua Wang, " A Parthenogenetic Algorithm for the Founder Sequence Reconstruction Problem," Journal of Computers vol. 8, no. 11, pp. 2934-2941, 2013.
General Information
ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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