Evolutionary Computation for Modeling and Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Daniel Ashlock",
  title =        "Evolutionary Computation for Modeling and
  publisher =    "Springer",
  year =         "2006",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-387-22196-0",
  URL =          "http://www.springerlink.com/content/978-0-387-22196-0",
  abstract =     "Evolutionary Computation for Optimisation and
                 Modelling 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 modelling, 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.
                 Written for: Undergraduate and graduate students",
  notes =        "GP in Chapter 12 ISAc, Chapter 13 graph based EA,
                 Chapter 14 cellular encoding",
  size =         "572 pages.

                 2010 available in paper back. ISBN-13: 978-1441919694",

Genetic Programming entries for Daniel Ashlock