Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems)

Rating:
Summary: Most fit book?
Review: 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.

Rating:
Summary: A book for anyone interested in AI
Review: I bought Genetic Programming (GP) I & II many years ago. While I have yet to find a useful application of Koza'a work to my problems, I think many of the ideas he introduces make significant ground in many AI areas. The GP community grows every year and this is the book that started it all. Definately worth a read.

Rating:
Summary: Excelente libro
Review: Puntos a favor: - descripción original de la GP - muchos ejemplos de su aplicabilidad - fácil de comprender

Puntos en contra: - código fuente en un ápendice - código fuente de solo 3 ejemplos simples - código en LISP, no usa CLOS - sin indicaciones de como portarlo a C++ - no incluye código de funciones autom. definidas - se concentra excesivamente en mediciones estadísticas y menos en la técnica de resolver problemas

Rating:
Summary: Genetic Programming
Review: 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.

Rating:
Summary: Genetic Programming
Review: 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.

Rating:
Summary: Weighty tome that shows a possible future direction for CS.
Review: The short history of computer science as a discipline has had two major concerns: the production of programs that are provably efficient, and the production of programs that are provably correct. "Genetic Programming" is, possibly, the beginning of a third stream in CS, the production of programs that are possibly neither efficient nor correct, but "fit" to perform a given task. A strange idea to computer scientists, perhaps, but consider the analogy with living creatures. Is a shark, a bee, or a turtle either "efficient" or "correct"? Perhaps, perhaps not; there doesn't seem to be a way to measure these concepts for something as complex as a living species. But they are "fit." They've been successful, as species, in their respective ecological niches for millions of years. Koza's big idea is the automatic generation of programs via mutation and selection, by analogy with living systems, and he's written a big book to go with the big idea (819 pages). Demonstrating creation of non-trivial programs by means of simulated mutation & selection is a major accomplishment. I'd rate the promise of this line of research as high, given that compute power becomes cheaper every year while human brain power becomes more expensive. Also, natural systems are resilient and adaptive to changes in the environment, while man-made software systems are all too fragile. This observation leads to the hope that "fit" programs may increase the robustness of the the computer networks on which so much now depends. One quibble: there is a thin book inside this fat book, trying to get out. The thin book would make the research more accessible to the average practicing programmer. Until such a "reader's edition" comes out, "Genetic Programming" is a unique resource volume.

Rating:
Summary: The essential reference for GP
Review: 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.

Rating:
Summary: The essential reference for GP
Review: 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.