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.
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