03471nam a22004215i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003100137040000900168082001400177100003000191245010600221264004600327300004500373336002600418337002600444338003900470347002400509505048500533520162801018650001702646650002902663650002002692650001602712650001702728650001602745650003302761650004602794650002002840710003402860773002002894776003602914856009902950978-0-387-31909-4DE-He21320260521091900.0cr nn 008mamaa100301s2006    xxu|    s    |||| 0|eng d  a9780387319094  a997803873190947 a10.1007/0-387-31909-32doi  cCICY04a518.12231 aAshlock, Daniel.eauthor.10aEvolutionary Computation for Modeling and Optimizationh[recurso electrónico] /cby Daniel Ashlock. 1aNew York, NY :bSpringer New York,c2006.  aXIX, 571 p. 163 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda0 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.  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. 0aMATHEMATICS. 0aARTIFICIAL INTELLIGENCE. 0aBIOINFORMATICS. 0aALGORITHMS.14aMATHEMATICS.24aALGORITHMS.24aAPPLICATIONS OF MATHEMATICS.24aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).24aBIOINFORMATICS.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z978038722196040uhttp://dx.doi.org/10.1007/0-387-31909-3zVer el texto completo en las instalaciones del CICY