How to solve it : modern heuristics
This book is the only source that provides comprehensive, current, and correct information on problem solving using modern heuristics. It covers classic methods of optimization, including dynamic programming, the simplex method, and gradient techniques, as well as recent innovations such as simulated annealing, tabu search, and evolutionary computation. Integrated into the discourse is a series of problems and puzzles to challenge the reader. The book is written in a lively, engaging style and is intended for students and practitioners alike. Anyone who reads and understands the material in the book will be armed with the most powerful problem solving tools currently known
1 online resource (xv, 467 pages) : illustrations
9783662041314, 3662041316
680608029
I What Are the Ages of My Three Sons?
1 Why Are Some Problems Difficult to Solve?
II How Important Is a Model?
2 Basic Concepts
III What Are the Prices in 7-11?
3 Traditional Methods
Part 1
IV What Are the Numbers?
4 Traditional Methods
Part 2
V What's the Color of the Bear?
5 Escaping Local Optima
VI How Good Is Your Intuition?
6 An Evolutionary Approach
VII One of These Things Is Not Like the Others
7 Designing Evolutionary Algorithms
VIII What Is the Shortest Way?
8 The Traveling Salesman Problem
IX Who Owns the Zebra?
9 Constraint-Handling Techniques
X Can You Tune to the Problem?
10 Tuning the Algorithm to the Problem
XI Can You Mate in Two Moves?
11 Time-Varying Environments and Noise
XII Day of the Week of January 1st
12 Neural Networks
XIII What Was the Length of the Rope?
13 Fuzzy Systems
XIV Do You Like Simple Solutions?
14 Hybrid Systems
15 Summary
Appendix A: Probability and Statistics
A.1 Basic concepts of probability
A.2 Random variables
A.2.1 Discrete random variables
A.2.2 Continuous random variables
A.3 Descriptive statistics of random variables
A.4 Limit theorems and inequalities
A.5 Adding random variables
A.6 Generating random numbers on a computer
A.7 Estimation
A.8 Statistical hypothesis testing
A.9 Linear regression
A.10 Summary
Appendix B: Problems and Projects
B.1 Trying some practical problems
B.2 Reporting computational experiments with heuristic methods
References
Electronic reproduction, [Place of publication not identified], HathiTrust Digital Library, 2010
University of Alberta Access (Unlimited Concurrent Users) from Springer
archive.org Free eBook from the Internet Archive
openlibrary.org Additional information and access via Open Library