Genetic Algorithms in Search, Optimization, and Machine Learning

Fülszöveg

. Genetic
Algorithms
in Searchf
Optimization &
Machine Learning
V
David E. Goldberg
with Foreword by John Holland
This text introduces the theory, operation, and application of genetic algorithms — search algo-
rithms based on the mechanics of natural selection and genetics. Although genetic algorithms
(GAs) are already considered to be an important methodology in the development of search and
machine-learning methods, only recently have they received attention in other research and
industrial circles. The reliance of GAs on biological metaphor, theory, and terminology, com-
bined with the lack of a basic introducton to the subject, has obscured them from potential users
and hidden their value as broadly applicable, powerful techniques that are both easy to under-
stand and to use.
Genetic Algorithms in Search, Optimization, and Machine Learning brings together for the
first time, in an informal, tutorial fashion, the computer techniques, mathematical tools, and... Tovább

Fülszöveg

. Genetic
Algorithms
in Searchf
Optimization &
Machine Learning
V
David E. Goldberg
with Foreword by John Holland
This text introduces the theory, operation, and application of genetic algorithms — search algo-
rithms based on the mechanics of natural selection and genetics. Although genetic algorithms
(GAs) are already considered to be an important methodology in the development of search and
machine-learning methods, only recently have they received attention in other research and
industrial circles. The reliance of GAs on biological metaphor, theory, and terminology, com-
bined with the lack of a basic introducton to the subject, has obscured them from potential users
and hidden their value as broadly applicable, powerful techniques that are both easy to under-
stand and to use.
Genetic Algorithms in Search, Optimization, and Machine Learning brings together for the
first time, in an informal, tutorial fashion, the computer techniques, mathematical tools, and
research results that will enable both students and practitioners to apply genetic algorithms to
problems in many fields; programmers, scientists, engineers, mathematicians, statisticians and
management scientists will all find interesting possibilities here. The book is suitable both for
course work and for self-study. Major concepts are illustrated with running examples, and major
algorithms are illustrated by Pascal computer programs. Chapters conclude with exercises and
computer assignments. No prior knowledge of GAs or genetics is assumed, and only a minimum of
computer programming and mathematics background is required.
does an exceptional job of making the methods of GAs and classifier systems available to a
wide audience "
From the Foreword Vissza