Genetic Algorithms: Principles and Perspectives: A Guide to GA TheoryGenetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems. |
Contents
| 1 | |
Basic Principles | 19 |
Schema Theory | 65 |
GAs as Markov Processes | 111 |
The Dynamical Systems Model | 141 |
Statistical Mechanics Approximations | 172 |
Other editions - View all
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory Colin Reeves,Jonathan E. Rowe No preview available - 2002 |
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory Colin Reeves,Jonathan E. Rowe No preview available - 2013 |
Common terms and phrases
allele analysis annealing applied approach approximation assume average fitness behaviour binary strings bitwise building blocks calculate Chapter chromosomes columns computational consider corresponding crossover crossover and mutation cumulants defined described dynamics effect of crossover eigenvalues eigenvectors élitist epistasis epistasis variance equations evolution evolutionary evolutionary algorithms example finite Firstly fitness function fitness landscape fitness values fixed points gene genetic algorithm given gives global optimum Gray code Hamming distance Hamming landscape hill-climber Holland idea integer interaction linear Markov chain mutation rate obtain offspring Onemax optimal parameter parents particular permutation population containing population vector possible predict probability distribution problem properties proportional selection random variable recombination representation sample Schema Theorem schemata search space selection and mutation sequence simple simplex solution solve stochastic strings of length structure subset Suppose theory tion trajectory transition matrix uniform population Vose Walsh coefficients Walsh transform zero