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020 _a9780387310305
_99780387310305
024 7 _a10.1007/9780387310305
_2doi
035 _avtls000330885
039 9 _a201509030220
_bVLOAD
_c201404121731
_dVLOAD
_c201404091508
_dVLOAD
_c201401311355
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA75.5-76.95
100 1 _aBrameier, Markus F.
_eautor
_9300584
245 1 0 _aLinear Genetic Programming /
_cby Markus F. Brameier, Wolfgang Banzhaf.
264 1 _aBoston, MA :
_bSpringer US,
_c2007.
300 _axiii, 315 páginas,
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aGenetic and Evolutionary Computation,
_x1932-0167
500 _aSpringer eBooks
505 0 _aFundamental Analysis -- Basic Concepts of Linear Genetic Programming -- Characteristics of the Linear Representation -- A Comparison with Neural Networks -- Method Design -- Linear Genetic Operators I — Segment Variations -- Linear Genetic Operators II — Instruction Mutations -- Analysis of Control Parameters -- A Comparison with Tree-Based Genetic Programming -- Advanced Techniques and Phenomena -- Control of Diversity and Variation Step Size -- Code Growth and Neutral Variations -- Evolution of Program Teams -- Epilogue.
520 _aLinear Genetic Programming examines the evolution of imperative computer programs written as linear sequences of instructions. In contrast to functional expressions or syntax trees used in traditional Genetic Programming (GP), Linear Genetic Programming (LGP) employs a linear program structure as genetic material whose primary characteristics are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. This volume investigates typical GP phenomena such as non-effective code, neutral variations and code growth from the perspective of linear GP. The text is divided into three parts, each of which details methodologies and illustrates applications. Part I introduces basic concepts of linear GP and presents efficient algorithms for analyzing and optimizing linear genetic programs during runtime. Part II explores the design of efficient LGP methods and genetic operators inspired by the results achieved in Part I. Part III investigates more advanced techniques and phenomena, including effective step size control, diversity control, code growth, and neutral variations. The book provides a solid introduction to the field of linear GP, as well as a more detailed, comprehensive examination of its principles and techniques. Researchers and students alike are certain to regard this text as an indispensable resource.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aBanzhaf, Wolfgang.
_eautor
_9300585
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387310299
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-31030-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c277638
_d277638