Genetic Algorithms in Search, Optimization, and Machine Learning

Front Cover
Addison-Wesley, Jan 1, 1989 - Computers - 412 pages
4 Reviews
A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.

From inside the book

What people are saying - Write a review

User Review - Flag as inappropriate

i need full book

Contents

Robustness of Traditional Optimization and Search Methods
2
Genetic Algorithms at Worka Simulation by hand
15
Who Shall Live and Who Shall Die? The Fundamental Theorem
28
Copyright

23 other sections not shown

Common terms and phrases

References to this book

All Book Search results »

About the author (1989)

DAVID E. GOLDBERG is Jerry S. Dobrovolny Distinguished Professor in Entrepreneurial Engineering at the University of Illinois at Urbana-Champaign, where he also serves as the Director of the Illinois Genetic Algorithms Laboratory. He received his BSE, MSE, and PhD, all in civil engineering, from the University of Michigan. From 1976 to 1980 he held a number of positions at Stoner Associates of Carlisle, Pennsylvania, including project engineer and marketing manager.?In 2004, Dr. Goldberg cofounded Nextumi, Inc. (www.nextumi.com), a Web-infrastructure firm, and now serves as Nextumi's Chief Scientist.

Bibliographic information