artificial intelligence

Yeah, its a big book...weighs a ton. However, only the first few chapters are concerned with the basic mechanisms of GP (should be familiar to anyone with a background in genetic algorithms or evolutionary computation). The rest of the book is chock full of examples on how to apply GP. These examples are essential and very welcome. I've found that I can usually find a solved problem in Koza that is similar to what I'm after, then I adapt it to my needs. This is a great reference, but don't be fooled into thinking this book is a tutorial. Think of it more as an exposition of GP with examples. For a tutorial, look somewhere else.

The book was very large but enjoyable and made the subject very clear and easy to understand. It explained the genetic programming algorithm very well and showed the results of many experiments to show applicability, limitations, and characteristics of the method.

There was some repetition in places, maybe because the author wanted to emphasize some points and also to remain understandable to persons who may read selected chapters or examples rather than from cover to cover, page by page.

Although the book states that Genetic Programming does not depend on the LISP language or features, it uses LISP as its exclusive language of choice. I would like to implement these generally very computationally intensive Genetic Programming Algorithms in a very fast and efficient way, which for me implies assembly language, and although the author gives good tips about making the algorithm run faster the implementation shown is all LISP and nothing else. I am also interested in using the algorithm to generate efficient, parsimonious, code. The author described the additional problems of parsimony, but gave no information on generation of fast code from S expressions. I will have to refer to some compiler books and my own experiments to go further in this area.

I look forward to experimenting with the subject and reading some of Dr. Koza's other books on the subject.

Genetic algorithms refer to computer programs that 'evolve' in ways similar to biological organisms. 'Natural selection' specifies the features of the solution to look for, strings of binary numbers (or other similar structures) are mated, with the combination of strings containing partial solutions often producing the most 'fit' results. Generation after generation of this process continues towards the 'evolution' of the desired features. Although this reference is quite long, it is quite readable, and can be shortened significantly by omitting a number of subsections as well as chapters not essential to the core concepts, as well as the detailed appendices. This reference shows that a variety of problems from different fields can be solved in terms of a computer program, of which genetic programming can be the means to find one or more such valid computer programs. It is relevant in that genetic programming is another way to effect computation, as well as providing insight with respect to evolution in nature.