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Moin,
ich habe kürzlich ein interessantes Buch über Genetische Programmierung entdeckt, dass ich euch nicht vorenthalten möchte. Es ist unter der Creative Commons Lizenz veröffentlicht worden und daher frei (im Sinne von Freiheit und im Sinne von Freibier) und kann von jedem verteilt werden. Wie der Titel wohl schon vermuten lässt, ist das Buch in englischer Sprache verfasst.

Eine kostenlose Kopie als PDF als auch eine "analoge" Version für etwa zehn Euro gibt es hier: http://www.lulu.com/content/2167025


Kurze Leseprobe:
(Chapter 1, Introduction)
The goal of having computers automatically solve problems is central to
artificial intelligence, machine learning, and the broad area encompassed by
what Turing called “machine intelligence” (Turing, 194. Machine learning
pioneer Arthur Samuel, in his 1983 talk entitled “AI: Where It Has Been
and Where It Is Going” (Samuel, 1983), stated that the main goal of the
fields of machine learning and artificial intelligence is:
“to get machines to exhibit behaviour, which if done by humans,
would be assumed to involve the use of intelligence.”
Genetic programming (GP) is an evolutionary computation (EC)1 tech-
nique that automatically solves problems without requiring the user to know
or specify the form or structure of the solution in advance. At the most
abstract level GP is a systematic, domain-independent method for getting
computers to solve problems automatically starting from a high-level state-
ment of what needs to be done.
Since its inception, GP has attracted the interest of myriads of people
around the globe. This book gives an overview of the basics of GP, sum-
marised important work that gave direction and impetus to the field and
discusses some interesting new directions and applications. Things continue
to change rapidly in genetic programming as investigators and practitioners
discover new methods and applications. This makes it impossible to cover
all aspects of GP, and this book should be seen as a snapshot of a particular
moment in the history of the field.

1.1 Genetic Programming in a Nutshell
In genetic programming we evolve a population of computer programs. That
is, generation by generation, GP stochastically transforms populations of
programs into new, hopefully better, populations of programs, cf. Figure 1.1.
GP, like nature, is a random process, and it can never guarantee results.
GP’s essential randomness, however, can lead it to escape traps which de-
terministic methods may be captured by. Like nature, GP has been very
successful at evolving novel and unexpected ways of solving problems. (See
Chapter 12 for numerous examples.)
The basic steps in a GP system are shown in Algorithm 1.1. GP finds out
how well a program works by running it, and then comparing its behaviour
to some ideal (line 3). We might be interested, for example, in how well a
program predicts a time series or controls an industrial process. This com-
parison is quantified to give a numeric value called fitness. Those programs
that do well are chosen to breed (line 4) and produce new programs for the
next generation (line 5). The primary genetic operations that are used to
create new programs from existing ones are:
• Crossover: The creation of a child program by combining randomly
chosen parts from two selected parent programs.
• Mutation: The creation of a new child program by randomly altering
a randomly chosen part of a selected parent program.