Genetics: A Guide to Basic Concepts and Problem Solving

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Simple, clear, and to the point.......1999-03-06

This text makes the basic principles behind genetics very understandable. There are short questions integrated into the text itself in addition to questions at the end of each chapter just to make sure you understand before moving on. It's very difficult to get lost in the information due to this fact. All questions are answered fully and completely at the back of the book, as opposed to short one word answers that don't explain why the reader may have gotten a question right or wrong. Highly recommended.

Genetic Programming III: Darwinian Invention and Problem Solving

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READ IT BEFORE REVIEWING PLEASE
And the future is...
Why Should You Buy This Book???
A hint of the future.....
Can computers be creative?

Genetic Programming III: Darwinian Invention and Problem Solving
John R. Koza , Forrest H. Bennett III , David Andre , and Martin A. Keane
Manufacturer: Morgan Kaufmann
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Binding: Hardcover

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ASIN: 1558605436

Book Description

Genetic programming is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, optimal control, classification, system identification, function learning, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.

Researchers in artificial intelligence, machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly growing field of genetic programming.

* Explains how the success of genetic programming arises from seven fundamental differences distinguishing it from conventional approaches to artificial intelligence and machine learning
* Describes how genetic programming uses architecture-altering operations to make on-the-fly decisions on whether to use subroutines, loops, recursions, and memory
* Demonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a system for automatically creating computer programs
* Presents the general-purpose Genetic Programming Problem Solver
* Focuses on the previously unsolved problem of analog circuit synthesis, presenting genetically evolved filters, amplifiers, computational circuits, a robot controller circuit, source identification circuits, a temperature-measuring circuit, a voltage reference circuit, and more
* Introduces evolvable hardware in the form of field-programmable gate arrays
* Includes an introduction to genetic programming for the uninitiated

Customer Reviews:

READ IT BEFORE REVIEWING PLEASE.......2004-07-06

my five stars are just to counteract the single star from the idiotic reviewer who gave the book one star w/o even bothering to read it. i haven't read this volume yet either, but i just ordered it and it's on its way; i'm certain i won't be disapointed. i'm a programmer and an artist and i use GP effectively to evolve forms both sonic and visual. just because you know your field(s) doesn't mean you can't benefit from a knowledge of evolutionary algorithms, quite the contrary. we have koza and friends to thank for a lot of inspirational work. i for one enjoy the interaction that i have with my algorithms, and since i'm the fitness function, forms crop up which never would have if either i or the machine were working alone. apologies for being guilty of the same crime as said reviewer but i feel in this case it's justified.

And the future is..........2002-03-14

Genetic programming is like a new Big Bang in computer universe.
Reach the automatic programming level is a revolution that will affect the way things are done today.

In a very cientifyc way, the book shows all the aspects of how to get ready for this evolution.

Why Should You Buy This Book???.......2002-01-26

Why this book, when there are several shorter books on GP, and its principle author, John Koza, has written two other, more general and equally voluminous books on GP? This book addresses how to evolve program architecture, that's why! Living organisms didn't grow arms and eyes through simple mutation. It required more subtle genetic operators. Traditional genetic operators (as used in genetic algorithms) may be sufficient for evolving solutions to optimization problems where the structure if not the specifics of each solution is pretty much the same. But to effectively evolve program structures, you need architecture altering genetic operators. This book provides motivations in computer science, foundations in biology, and explanations in English.

A hint of the future............2001-07-28

The authors have written a fine book here and it has and will continue to be a source of good information on the subject. What is most interesting about the approach of genetic programming is that it does not make use of the inference methods of formal logic in the search for the correct program. Correctly observing that logical thinking is insufficient for invention and creativity, the authors follow the "logic considered harmful" philosophy in their attempts to get a computer to find a creative/original solution to a problem. And most importantly, they discuss fourteen examples where genetic programming has produced results that are competitive with human-produced results. The book is almost 1200 pages long, but without reading all the examples one could cover the main points in a reasonable time frame. The reader knowing the LISP language will appreciate the discussion more.

After a brief introduction to the book in chapter 1, the authors move on to a detailed discussion of the philosophy and approaches used in genetic programming. They list the five steps that must be done before applying a genetic algorithm to a problem and give an overview of the LISP background needed to understand genetic programming. The authors emphasize that the genetic algorithm is probabilistic in nature, with the initial populations, individual selection, and genetic operation chosen at random. They give flowcharts illustrating a typical genetic algorithm and program, and then show executable programs can be automatically created. A very extensive list of references on genetic programming is given at the end of the chapter.

In the next part, the authors discuss how to eliminate the requirement that the programmer specify the architecture in advance to the program to be created. After reviewing some methods that were previously used to make the choice of architecture, the authors move on to describing a set of architecture-altering operations that give an automated method for determining the architectures of evolving programs. The discussion on automatically defined recursion is particularly interesting.

The book then shows how to use the results so far to allow problem-solving to be done using genetic programming, the first one being the rotation of automobile tires and the second being evolving a computer program with the behavior of Boolean even-parity functions. This is followed by a discussion of how to use architecture-altering operations to solve a time-optimal control problem. The most interesting part of this discussion is that it illustrates the important point that disadvantageous actions should be taken in the short term so that the long-term objective can be achieved.

In chapter 14, the ant foraging problem is used to illustrate a form of the (Minsky) multiagent problem and architecture-altering operations. This is followed by discussions on the digit recognition problem and the transmembrane segment identification problem. The authors choose the Fibonacci sequence to illustrate how recursion can be used in solving problems with genetic programming. The necessity of using internal storage is illustrated using the cart centering problem.

The authors then overview the use of the Genetic Programming Problem Solver (GPPS) for automatically creating a computer program to solve a problem. Several problems are examined using this Solver, such as symbolic regression, sorting networks, and the intertwined spirals problem.

The next part then considers the application of genetic programming to the automated synthesis of analog electrical circuits. The authors judge, rightfully, that the design process is one that will be a good judge of automated technique versus one that was done by humans, especially considering the fact that analog design is considered by many to be an "art" rather than a "science". The authors show how to import the SPICE simulation system into the genetic programming system, and discuss how validation of circuit design using this simulator would be done by the genetic programming system. After showing how a low-pass filter may be successfully designed using the genetic programming system, the authors show how with a few changes it can be used to design many different types of circuits. Interestingly, the authors cite the rediscovery by genetic programming of the elliptic filter topology of W. Cauer. Cauer arrived at his discovery via the use of elliptic functions, but the genetic program did not make use of these, but relied solely on the problem's fitness measure and natural selection!

An interesting discussion is also given of the role of crossover in genetic programming by comparing the problem of synthesizing a lowpass filter with and without using crossover. The authors conclude that the crossover operation plays a large contribution to the actual solution of the problem.

Then later, the authors show how genetic programming actually evolved a cellular automata that performs better than a succession of algorithms written by humans in the last two decades. Specifically, they show how genetic programming evolved a rule for the majority classification problem for one-dimensional two-state cellular automata that exceeds the accuracy of all known rules.

Most interestingly, the authors show how genetic programming evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese superoxide dismutase family.

The actual performance and implementation issues involved in genetic programming are discussed in the last two parts of the book. They discuss the computer time needed to yield the 14 instances where they claim that genetic programming has produced results that are competitive with human-produced results.

The authors wrap things up in the last chapter of the book and discuss other instances where genetic programming has succeeded in automatically producing computer programs that are competitive with human-produced results. The evidence they have in the book is impressive but there are a few areas that will be ultimate tests of this approach, the most important being the discovery of new mathematical results or algorithms. It is this area that requires the most creativity on the part of the inventor.

Can computers be creative?.......1999-12-03

The quest for *Automatic Programming* is the holy grail of artificial intelligence. The dream of having computer programs write other useful computer programs has haunted researchers since the nineteen fifties. In Genetic Programming III -Darwinian Invention and problem solving (GP3) by John R. Koza, Forest H Bennet III, David Andre and Martin A Keane, the authors claim that the first inscription on this trophy should be the name Genetic Programming (GP). GP is about applying evolutionary algorithms to search the space of computer programs. The authors paraphrase Arthur Samuel of 1959 and argue that with this method it is possible to "tell the computer what to do without telling it explicitly how to do it".

The main hypothesis of the book is that GP is not only the first instance of true automatic programming but also creative to such an extant that it competes with humans in solving very hard problems and therefore the solutions produced by GP can sometimes be called inventions, thus the name "Darwinian Invention Machine". The book starts by listing sixteen proposed attributes of any automatic programming system. The attribute list begins with obvious properties such as the ability to produce entities that can run on a computer, continues by describing components of full computer programs and ends by expressing fuzzier concepts such as applicability, scalability and competitiveness with human-produced results. The authors argue that GP definitely has most of the 16 attributes and at least to some extent possesses the remaining few. The last attribute, human competitive results, is in turn defined by a list of eight properties where each of them gives enough evidence to conclude competitiveness to results produced by the intellect of a human. This list includes concepts such as whether the results are pantentable, publishable in scientific journals or better then best known human solutions. GP3 reports 14 experiments by the authors where the they claim that GP produced results fulfilling one or more of these properties and thus are competitive with that of a skilled human such as an engineer, mathematician, designer or programmer. Examples of results with the "darwinian invention quality" include sorting networks, analogue electrical circuit synthesis and creation of motifs for protein family detection. Pointers are also given to human competitive solutions evolved by other researchers.

Overall there is no question that this is an important book putting the spotlight on one of the peak performing and most promising candidates for the general AI prize. There is no doubt that this book belongs in the standard library of all GP researchers or practitioners. This volumous book is a bit heterogeneous, probably stemming from the fact that is combined from a number of previously published papers with some new material. On the other hand is the volume important documentation of innovative work done by John Koza and his colleagues. In many place numerous pointers to work by other researchers are given but in the end I believe that the book would have a stronger case for presenting the GP state-of-the-art by including more references to similar research by other research groups.

Book Description

Genetic Programming Theory and Practice IV was developed from the fourth workshop at the University of Michigan’s Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.

This volume represents a watershed moment in the GP field in that GP has begun to move from hand-crafted software used primarily in academic research, to an engineering methodology applied to commercial applications. It is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic Algorithms and Evolutionary Computation)

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Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic Algorithms and Evolutionary Computation)
Carlos A. Coello Coello , David A. Van Veldhuizen , and Gary B. Lamont
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0306467623

Book Description

The solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications. Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter.

For additional information and supplementary teaching materials, please visit the authors' website at http://www.cs.cinvestav.mx/~EVOCINV/bookinfo.html.

Train the Wolf in Your Dog: Genetic Clues to Solving Behavior Problems

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Train the Wolf in Your Dog: Genetic Clues to Solving Behavior Problems
Diane Morgan
Manufacturer: Doral Publishing
ProductGroup: Book
Binding: Paperback

General | Animal Care & Pets | Home & Garden | Subjects | Books
ASIN: 0974540722

Book Description

Weighing 2 to 200 pounds the Canis Lupus Familiaris can kill you or save your life. This book will show you how to stop the unnecessary wreckage of your home and accidental bites. Tips on how to prevent neglect, abuse and unintentional harm to the canine we call friend can be found in this endearing book.

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Approach to Problem Solving in Genetics
D. Englent
Manufacturer: Stipes Publishing, LLC
ProductGroup: Book
Binding: Paperback

Genetics | Evolution | Science | Subjects | Books

Artificial Neural Nets. Problem Solving Methods: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, Maó, Menorca, ... Part II (Lecture Notes in Computer Science)

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Artificial Neural Nets. Problem Solving Methods: 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, Maó, Menorca, ... Part II (Lecture Notes in Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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ASIN: 354040211X

Book Description

The two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in Maó, Menorca, Spain in June 2003.

The 197 revised papers presented were carefully reviewed and selected for inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.

Business Genetics: Understanding 21st Century Corporations using xBML

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Business Genetics: Understanding 21st Century Corporations using xBML
Cedric G. Tyler , and Stephen R. Baker
Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

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Building an Enterprise Architecture Practice: Tools, Tips, Best Practices, Ready-to-Use Insights (The Enterprise Series)

ASIN: 0470066547

Book Description

A revolutionary way to describe business, xBML (extended Business Modelling Language) is an intuitive graphical language that unlocks the DNA of a corporation using a system of diagrams based on five Ws (Who; What; Which; Where; When). xBML gives companies an complete and accurate map of their enterprise, that can then be re-used repeatedly to describe, plan and create improvement.

Book Description

The robust capability of Evolutionary Algorithms (EAs) to find solutions to difficult problems has permitted them to become the optimization and search techniques of choice for many practical static problems.

Despite this success in many different environments, EAs are often prone to failure when subjected to even small changes in the problem. Effective solutions for many real-world engineering and economic problems require systems that adapt to changes over time.

This book addresses the issues involved in the design of EAs that successfully operate in dynamic environments without human intervention, and provides a method for creating EAs for these environments.

Evolution and optimization: An introduction to solving complex problems by replicator networks (Mathematical ecology)

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Evolution and optimization: An introduction to solving complex problems by replicator networks (Mathematical ecology)
Hans-Michael Voigt
Manufacturer: Akademie-Verlag
ProductGroup: Book
Binding: Unknown Binding

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ASIN: 3055006178

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