An Introduction to Genetic Algorithms

Table of Contents:
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.

Review :
"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.