An Introduction to Genetic Algorithms

Front Cover
MIT Press, 1998 - Computers - 209 pages
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Genetic algorithms have been used in science and engineering as adaptive algorithmsfor solving practical problems and as computational models of natural evolutionary systems. Thisbrief, accessible introduction describes some of the most interesting research in the field and alsoenables readers to implement and experiment with genetic algorithms on their own. It focuses indepth 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 workof Mitchell and her colleagues.

The descriptions of applications and modelingprojects stretch beyond the strict boundaries of computer science to include dynamical systemstheory, game theory, molecular biology, ecology, evolutionary biology, and population genetics,underscoring the exciting "general purpose" nature of genetic algorithms as search methods that canbe employed across disciplines.

An Introduction to GeneticAlgorithms is accessible to students and researchers in any scientific discipline. Itincludes many thought and computer exercises that build on and reinforce the reader's understandingof the text.

The first chapter introduces genetic algorithms and their terminologyand describes two provocative applications in detail. The second and third chapters look at the useof genetic algorithms in machine learning (computer programs, data analysis and prediction, neuralnetworks) and in scientific models (interactions among learning, evolution, and culture; sexualselection; ecosystems; evolutionary activity). Several approaches to the theory of geneticalgorithms 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 futureof evolutionary computation.

  

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DETALS ABOUT GENETIC ALGORITHMS (BASIC PART OF GA)

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what is the pic on the frontpage?

Contents

An Overview
1
Genetic Algorithms in Problem Solving
35
Genetic Algorithms in Scientific Models
85
Theoretical Foundations of Genetic Algorithms
117
Implementing a Genetic Algorithm
155
Conclusions and Future Directions
181
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