000			03971nam a22005293i 4500
001			EBC3020247
003			MiAaPQ
005			20240729124135.0
006			m o d |
007			cr cnu||||||||
008			240724s2010 xx o ||||0 eng d
020			_a9781616683740 _q(electronic bk.)
020			_z9781616681708
035			_a(MiAaPQ)EBC3020247
035			_a(Au-PeEL)EBL3020247
035			_a(CaPaEBR)ebr10676408
035			_a(OCoLC)923663273
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ
050		4	_aQH445.2 -- .D57 2010eb
082	0		_a572.8/633
100	1		_aMester, D.
245	1	0	_aDiscrete Optimization for TSP-like Genome Mapping Problems.
250			_a1st ed.
264		1	_aHauppauge : _bNova Science Publishers, Incorporated, _c2010.
264		4	_c©2010.
300			_a1 online resource (73 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aGenetics - Research and Issues
505	0		_aIntro -- DISCRETE OPTIMIZATION FOR SOME TSP-LIKE GENOME MAPPING PROBLEMS -- DISCRETE OPTIMIZATION FOR SOME TSP-LIKE GENOME MAPPING PROBLEMS -- CONTENTS -- PREFACE -- Chapter 1 GENERAL INTRODUCTION -- Chapter 2 GUIDED EVOLUTION STRATEGY ALGORITHM FOR CLASSIC TSP AS A BASIS FOR SOLVING THE GENETIC/GENOMIC TSP-LIKE PROBLEMS -- 2.1. Introduction -- 2.2. The Problem Solving Methodology -- 2.2.1. The Improvement Heuristics -- 2.2.2. Phase 1: A Guided Local Search Metaheuristic for the TSP -- 2.2.3. Phase 2: Attempting Small Mutations -- 2.3. Experimental results -- 2.3.1. The Problem Data and Parameter Setting -- 2.3.2. Analysis of Different Algorithm Configurations -- 2.3.3. Results for Standard TSP Benchmarks -- Chapter 3 MULTILOCUS GENETIC MAPPING -- 3.1. Introduction -- 3.2. Evolution Strategies For Combinatorial Optimization Problems -- 3.3. The Evolution Strategy Algorithm with Multi-Parametric Mutator (ES-MPM) -- 3.4. Guided Evolution Strategies (GES) for MGM -- 3.5. Experiments on MGM Using Simulated Datasets -- 3.6. An Approach to Increase the Map Reliability by Using Verification Process -- Chapter 4 MULTILOCUS CONSENSUS GENETIC MAPPING: FORMULATION, MODEL AND ALGORITHMS -- 4.1. Introduction -- 4.2. Main Idea of the Approach to Solve MCGM -- 4.3. Full Frame (FF) Algorithm for MCGM -- 4.4. Specific Conflicted Frames (SCF) Algorithm for MCGM -- Chapter 5 TSP-LIKE PROBLEM IN PHYSICAL MAPPING (PMP) -- 5.1. Introduction -- 5.1.1. The Model and Problem Formulation -- 5.1.2. Standard Methodology for Solving Physical Mapping Problem -- 5.1.3. An Alternative Approach to Contig Assembly -- 5.2. Reducing PMP to Standard TSP -- 5.2.1. Clustering Algorithm -- 5.2.2. Looking for Linear Topological Structure -- 5.2.3. Multipoint Ordering Using Global Optimization -- 5.2.4. Re-Sampling Verification of the Obtained Solution.
505	8		_a5.2.5. Merging of Sub-Contigs -- Chapter 6 CONCLUSIONS -- ACKNOWLEDGMENT -- APPENDIX. CHOOSING THRESHOLD Q VALUE FOR PRC CALCULATION -- References -- INDEX -- Blank Page.
588			_aDescription based on publisher supplied metadata and other sources.
590			_aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650		0	_aGene mapping -- Mathematical models.
650		0	_aMathematical optimization.
655		4	_aElectronic books.
700	1		_aRonin, D.
700	1		_aKorostishevsky, M.
700	1		_aKorol, A.
700	1		_aBraysy, Z.
700	1		_aRaa, W.
776	0	8	_iPrint version: _aMester, D. _tDiscrete Optimization for TSP-like Genome Mapping Problems _dHauppauge : Nova Science Publishers, Incorporated,c2010 _z9781616681708
797	2		_aProQuest (Firm)
830		0	_aGenetics - Research and Issues
856	4	0	_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=3020247 _zClick to View
999			_c60776 _d60776