000 03652nam a22004935i 4500
001 978-0-387-31909-4
003 DE-He213
005 20161121231025.0
007 cr nn 008mamaa
008 100301s2006 xxu| s |||| 0|eng d
020 _a9780387319094
_9978-0-387-31909-4
024 7 _a10.1007/0-387-31909-3
_2doi
050 4 _aQA76.9.A43
072 7 _aPBKS
_2bicssc
072 7 _aCOM051300
_2bisacsh
082 0 4 _a518.1
_223
100 1 _aAshlock, Daniel.
_eauthor.
245 1 0 _aEvolutionary Computation for Modeling and Optimization
_h[electronic resource] /
_cby Daniel Ashlock.
264 1 _aNew York, NY :
_bSpringer New York,
_c2006.
300 _aXX, 572 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_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 _aApplied mathematics.
650 0 _aEngineering mathematics.
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
912 _aZDB-2-SMA
950 _aMathematics and Statistics (Springer-11649)
999 _c507450
_d507450