The articles presented here were selected from preliminary versions presented at the
International Conference on Genetic Algorithms in June 1991 as well as at a special Workshop
on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are
general-purpose search algorithms that use principles inspired by natural population genetics
to evolve solutions to problems. The basic idea is to maintain a population of knowledge
structure that represent candidate solutions to the problem of interest. The population evolves
over time through a process of competition (i.e. survival of the fittest) and controlled
variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains
articles on three topics that have not been the focus of many previous articles on GAs namely
concept learning from examples reinforcement learning for control and theoretical analysis of
GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine
learning community with the major areas of work on GAs. The articles in this book address a
number of central issues in applying GAs to machine learning problems. For example the choice
of appropriate representation and the corresponding set of genetic learning operators is an
important set of decisions facing a user of a genetic algorithm. The study of genetic
algorithms is proceeding at a robust pace. If experimental progress and theoretical
understanding continue to evolve as expected genetic algorithms will continue to provide a
distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited
volume of original research made up of invited contributions by leading researchers.
