Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications

by Michael Affenzeller, Stephan Winkler, Stefan Wagner

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.

394 pages, Hardcover

First published January 15, 2008

Reviews

[Marcin · Rating 5 out of 5]

Though it might seem like a common conference proceedings mixed into chapters kind of book, truth is - it is quite thorough, full of details and short insights, enough to make an amateur interested and information filled and a connoiseur not lose temper from overabundance of text fillers. The discussion of GP basics, especially with the usage of the method in mind, is done simply to level the readers' knowledge. What follows is the discussion of the GA and their strengths used in GP to master nature's technique in performing various, but mostly numerical optimisation, tasks. The Author provides a little bit of empirical analyses concerning the benefits and the fallbacks of GP. And how to administer which algorithms where to actually reduce errors and improve on the combined methods' strengths. What the book provides among only selected handful other positions, is the focus on information transfer with a fact right from the start that the GA method does not mean, in effect, obtaining Tron 2 programs mixed with Matrix agents and that through GA/GP one cannot solve every problem without proper formalisation. So yes, one can get a program that would provide you with the most important answer for the most important question of all, but whether it is correct or not depends on how the problem/solution space was presented. And whether the GP system was given enough time - it took millions of years for mother nature to obtain living programs beautifully adapted to their problem environments.