This invaluable book has been designed to be useful to most practising scientists and engineers, whatever their field and however rusty their mathematics and programming might be. The approach taken is largely practical, with algorithms being presented in full and working code (in Basic, Fortran, Pascal And C) included on a floppy disk to help the reader get up and running as quickly as possible. The text could also be used as part of an undergraduate course on search and optimisation. Student exercises are included at the end of several of the chapters, many of which are computer-based and designed to encourage exploration of the method.
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Most Helpful Customer Reviews
16 of 17 people found the following review helpful:
2.0 out of 5 stars
Too little information, even for beginners,
By Emre Celebi (TX, USA) - See all my reviews
This review is from: Introduction to Genetic Algorithms for Scientists and Engineers (Paperback)
This is an introductory (undergraduate level) book targeted towards practitioners. The content is far from being satisfactory, even for beginners. However, if you have only a couple of hours and you want to get some information about GAs, this book is for you. If you're looking for comprehensive coverage on the topic, I'd recommend Eiben & Smith's "Introduction to Evolutionary Computing".
12 of 13 people found the following review helpful:
5.0 out of 5 stars
Get started with GAs fast,
By
This review is from: Introduction to Genetic Algorithms for Scientists and Engineers (Paperback)
The best introduction to GAs for those wishing to get up and running and using such algorithms to solve real problems. The software provided seems to work well and just about anyone will understand the book. My only complaint is that the examples could have covered a better range of topics.
7 of 7 people found the following review helpful:
4.0 out of 5 stars
An honest book,
By
This review is from: Introduction to Genetic Algorithms for Scientists and Engineers (Paperback)
A fine introduction. Well written, very, very clear... And the codes are pretty easy to understand even for beginners. I would recommend it as a first course on GAs.
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Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Inside This Book
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First Sentence:
Genetic algorithms (GAs) are numerical optimisation algorithms inspired by both natural selection and natural genetics. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
linear fitness scaling, plane illuminance, fitness proportional selection, possible schemata, unfeasible solutions, nondominated solutions, fitness sharing, single point crossover, temporary population, function optimisation, water distribution networks, genetic algorithms, many local optima, fittest individual, uniform crossover, implicit parallelism, search space Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, New York, Water Resour, Complex Systems, Generation Figure, University of Exeter, San Mateo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Genetic algorithms (GAs) are numerical optimisation algorithms inspired by both natural selection and natural genetics. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
linear fitness scaling, plane illuminance, fitness proportional selection, possible schemata, unfeasible solutions, nondominated solutions, fitness sharing, single point crossover, temporary population, function optimisation, water distribution networks, genetic algorithms, many local optima, fittest individual, uniform crossover, implicit parallelism, search space Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, New York, Water Resour, Complex Systems, Generation Figure, University of Exeter, San Mateo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Flap | Back Cover | Surprise Me!
Citations (learn more)
This book cites 18
books:
See all 18 books this book cites
5
books
cite this book:
- Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life by Paul Bourgine on page 207, and page 215
- Problem Solving (Teacher Timesavers) by Jill Bennett on page 219
- Contemporary Aesthetics (Modern Revivals in Philosophy) by R. A. Sharpe on page 219
- Evolutionary Computation by Kenneth A. de De Jong on page 220
- Problem Solving (Brooks/Cole One-Unit Series in Precalculus Mathematics) by Karl J. Smith on page 219
See all 18 books this book cites
- Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques by Edmund K. Burke on page 118
- Recent Advances In Simulated Evolution And Learning (Advances in Natural Computation) by K. C. Tan on page 621
- Principles of Nanotechnology: Molecular-based Study of Condensed Matter in Small Systems by G Ali Mansoori on page 190
- Software Engineering with Computational Intelligence (The Springer International Series in Engineering and Computer Science) by Taghi M. Khoshgoftaar on page 286
- Practice and Theory of Automated Timetabling III: Third International Conference, PATAT 2000 Konstanz, Germany, August 16-18, 2000 Selected Papers (Lecture Notes in Computer Science) by Edmund Burke on page 273
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