Genetic programming may be more powerful than neural networks and other machine learning techniques, able to solve problems in a wider range of disciplines. In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of program induction, and that the recently developed genetic programming paradigm provides a way to search the space of possible computer programs for a highly fit individual computer program to solve the problems of program induction. Good programs are found by evolving them in a computer against a fitness measure instead of by sitting down and writing them.John R. Koza is Consulting Associate Professor in the Computer Science Department at Stanford University.
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The research reported in this book is a tour de force. For the first time, since the idea was bandied about in the '40s and early '50s, we have a non-trivial, nontailored set of examples of automatic programming." John Holland
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4 of 4 people found the following review helpful
By A Customer
Format:Hardcover
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.
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.
By O. Odjoh
Format:Hardcover
This book is quite dense, I would say it is highly appropriate if you are a research student; it is not for those who are time poor and need to get something underway ASAP.
1 of 6 people found the following review helpful
By A Customer
Format:Hardcover
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
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