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An Introduction to Genetic Algorithms

By: Material type: TextTextPublication details: New Delhi Prentice-Hall of India 2002Edition: [ Eastern Economy Edition ]Description: viii, 209 pages : illustrations ; 26 cmISBN:
  • 8120313585
Subject(s): DDC classification:
  • 575.10113 MIT-I
Contents:
1. Genetic Algorithms: An Overview --- 2. Genetic Algorithms in Problem Solving --- 3. Genetic Algorithms in Scientific Models --- 4. Theoretical Foundations of Genetic Algorithms --- 5. Implementing a Genetic Algorithm --- 6. Conclusions and Future Directions.
Summary: 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 modeling, and artificial life_and reviews a broad span of research, including the work of Mitchell and her colleagues.
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Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 575.10113 MIT-I (Browse shelf(Opens below)) Available DCB15

1. Genetic Algorithms: An Overview --- 2. Genetic Algorithms in Problem Solving --- 3. Genetic Algorithms in Scientific Models --- 4. Theoretical Foundations of Genetic Algorithms --- 5. Implementing a Genetic Algorithm --- 6. Conclusions and Future Directions.

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 modeling, and artificial life_and reviews a broad span of research, including the work of Mitchell and her colleagues.

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