000			03572nam a22004455i 4500
001			978-0-387-31909-4
003			DE-He213
005			20250710083949.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387319094 _a99780387319094
024	7		_a10.1007/0-387-31909-3 _2doi
082	0	4	_a518.1 _223
100	1		_aAshlock, Daniel. _eauthor.
245	1	0	_aEvolutionary Computation for Modeling and Optimization _h[recurso electrónico] / _cby Daniel Ashlock.
264		1	_aNew York, NY : _bSpringer New York, _c2006.
300			_aXIX, 571 p. 163 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aAn Overview of Evolutionary Computation -- Designing Simple Evolutionary Algorithms -- Optimizing Real-Valued Functions -- Sunburn: Coevolving Strings -- Small Neural Nets : Symbots -- Evolving Finite State Automata -- Ordered Structures -- Plus-One-Recall-Store -- Fitting to Data -- Tartarus: Discrete Robotics -- Evolving Logic Functions -- ISAc List: Alternative Genetic Programming -- Graph-Based Evolutionary Algorithms -- Cellular Encoding -- Application to Bioinformatics.
520			_aEvolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool.
650		0	_aMATHEMATICS.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aBIOINFORMATICS.
650		0	_aALGORITHMS.
650	1	4	_aMATHEMATICS.
650	2	4	_aALGORITHMS.
650	2	4	_aAPPLICATIONS OF MATHEMATICS.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aBIOINFORMATICS.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387221960
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-31909-3 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c57156 _d57156