An introduction to genetic algorithms
著者
書誌事項
An introduction to genetic algorithms
（Complex adaptive systems）（Bradford book）
MIT Press, c1996
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注記
Includes bibliographical references (p.[191]-201) and index (p.[203]-205)
Third printing 1997 ; 209p
内容説明・目次
内容説明
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics - particularly in machine learning, scientific modelling and artificial life - and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modelling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. "An Introduction to Genetic Algorithms" is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercies that build on and reinforce the reader's understanding of the text.
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programmes, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection, ecosystems; and evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
目次
- 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.
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