Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
(Depends on publisher)
This book describes several generic algorithmic concepts that can be used in any kind of GA or with evolutionary optimization techniques. It provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, the authors show how to substantially increase achievable solution quality.
| Quantity | Price | Discount |
|---|---|---|
| List Price | $94.95 |
$94.95
Book Information
| Publisher: | CRC Press |
|---|---|
| Publish Date: | 04/16/2018 |
| Pages: | 400 |
| ISBN-13: | 9781138114272 |
| ISBN-10: | 1138114278 |
| Language: | English |
Full Description
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development.
The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems.
Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.