000			04174nam a22005175i 4500
001			978-0-387-77610-1
003			DE-He213
005			20251006084415.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387776101
020			_a99780387776101
024	7		_a10.1007/978-0-387-77610-1 _2doi
100	1		_aDorronsoro, Bernabe. _eauthor.
245	1	0	_aCellular Genetic Algorithms _h[electronic resource] / _cby Bernabe Dorronsoro, Enrique Alba.
264		1	_aBoston, MA : _bSpringer US, _c2008.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aOperations Research/Computer Science Interfaces Series, _x1387-666X ; _v42
505	0		_aI Introduction -- to Cellular Genetic Algorithms -- The State of the Art in Cellular Evolutionary Algorithms -- II Characterizing Cellular Genetic Algorithms -- On the Effects of Structuring the Population -- Some Theory: A Selection Pressure Study on cGAs -- III Algorithmic Models and Extensions -- Algorithmic and Experimental Design -- Design of Self-adaptive cGAs -- Design of Cellular Memetic Algorithms -- Design of Parallel Cellular Genetic Algorithms -- Designing Cellular Genetic Algorithms for Multi-objective Optimization -- Other Cellular Models -- Software for cGAs: The JCell Framework -- IV Applications of cGAs -- Continuous Optimization -- Logistics: The Vehicle Routing Problem -- Telecommunications: Optimization of the Broadcasting Process in MANETs -- Bioinformatics: The DNA Fragment Assembly Problem.
520			_aCELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability. The methods are benchmarked against well-known metaheutistics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of "vehicle routing" and the hot topics of "ad-hoc mobile networks" and "DNA genome sequencing" to clearly illustrate and demonstrate the power and utility of these algorithms.
650		0	_aECONOMICS.
650		0	_aGENETICS _xMATHEMATICS.
650		0	_aALGORITHMS.
650		0	_aMATHEMATICAL OPTIMIZATION.
650		0	_aBUSINESS LOGISTICS.
650	1	4	_aECONOMICS/MANAGEMENT SCIENCE.
650	2	4	_aMATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS.
650	2	4	_aOPTIMIZATION.
650	2	4	_aPRODUCTION/LOGISTICS.
650	2	4	_aALGORITHMS.
650	2	4	_aGENETICS AND POPULATION DYNAMICS.
650	2	4	_aOPERATIONS RESEARCH/DECISION THEORY.
700	1		_aAlba, Enrique. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387776095
830		0	_aOperations Research/Computer Science Interfaces Series, _x1387-666X ; _v42
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-77610-1 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SBE
942			_2ddc _cER
999			_c58989 _d58989