Genetic algorithms - an overview: a brief history of evolutionary computation; the appeal of evolution; biological terminology; search spaces and fitness landscapes; elements of genetic algorithms; a simple genetic algorithm; genetic algorithm and traditional search methods; some applications of genetic algorithms; two brief examples; how do genetic algorithms work?; thought exercises; computer exercises. Genetic algorithms in problem solving: evolving computer programs; data analysis and prediction; evolving neural networks; thought exercises; computer exercises. Genetic algorithms in scientific models: modeling interactions between learning and evolution; modeling sexual selection; modeling ecosystems; measuring evolutionary activity; thought exercises; computer exercises. Theoretical foundations of genetic algorithms: schemas and the two-armed bandit problem; royal roads; exact mathematical models of simple genetic algorithms; statistical mechanics approaches; thought exercises; computer exercises. Implementing a genetic algorithm: when should a genetic algorithm be used? encoding a problem for a genetic algorithm; adapting the encoding; selection methods; genetic operators; parameters for genetic algorithms; thought exercises; computer exercises. Conclusion and future directions.
"This is the best general book on Genetic Algorithms written to
date. It covers background, history, and motivation; it selects
important, informative examples of applications and discusses the
use of Genetic Algorithms in scientific models; and it gives a good
account of the status of the theory of Genetic Algorithms. Best of
all the book presents its material in clear, straightforward,
felicitous prose, accessible to anyone with a college-level
scientific background. If you want a broad, solid understanding of
Genetic Algorithms--where they came from, what's being done with
them, and where they are going--this is the book.--John H. Holland,
Professor, Computer Science and Engineering, and Professor of
Psychology, The University of Michigan; External Professor, the
Santa Fe Institute.
& quot; This is the best general book on Genetic Algorithms written
to date. It covers background, history, and motivation; it selects
important, informative examples of applications and discusses the
use of Genetic Algorithms in scientific models; and it gives a good
account of the status of the theory of Genetic Algorithms. Best of
all the book presents its material in clear, straightforward,
felicitous prose, accessible to anyone with a college-level
scientific background. If you want a broad, solid understanding of
Genetic Algorithms -- where they came from, what's being done with
them, and where they are going -- this is the book. -- John H.
Holland, Professor, Computer Science and Engineering, and Professor
of Psychology, The University of Michigan; External Professor, the
Santa Fe Institute.
" This is the best general book on Genetic Algorithms written to
date. It covers background, history, and motivation; it selects
important, informative examples of applications and discusses the
use of Genetic Algorithms in scientific models; and it gives a good
account of the status of the theory of Genetic Algorithms. Best of
all the book presents its material in clear, straightforward,
felicitous prose, accessible to anyone with a college-level
scientific background. If you want a broad, solid understanding of
Genetic Algorithms -- where they came from, what's being done with
them, and where they are going -- this is the book. -- John H.
Holland, Professor, Computer Science and Engineering, and Professor
of Psychology, The University of Michigan; External Professor, the
Santa Fe Institute.
-- John H. Holland, Professor, Computer Science and Engineering,
and Professor of Psychology, The University of Michigan; External
Professor, the Santa Fe Institute.
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