Download Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza
Also there are numerous books to select; you may feel so hard to pick which one that is extremely ideal for you. Nevertheless, when you still feel overwhelmed, take the Genetic Programming III: Darwinian Invention And Problem Solving (Vol 3), By John R. Koza as your reference to read now. The soft documents will certainly worry about the same points with the print file. We provide this publication is just for you who wish to try analysis. Also you have no analysis practice; it can be starter way to love analysis.
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza
Download Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza
After waiting on some minutes, finally we could provide Genetic Programming III: Darwinian Invention And Problem Solving (Vol 3), By John R. Koza in this internet site. This is one of the books that mostly most waited and also wanted. Investing even more times to await this book will certainly not be issue. You will certainly also discover the right way to confirm the number of individuals talk about this publication. After the launching, this publication can be located in several sources.
We know and realize that often publications will certainly make you really feel bored. Yeah, spending many times to only read will specifically make it real. Nevertheless, there are some methods to conquer this issue. You can just spend your time to check out in few pages or only for filling the spare time. So, it will certainly not make you feel burnt out to constantly face those words. As well as one essential thing is that this publication offers extremely intriguing topic to read. So, when reviewing Genetic Programming III: Darwinian Invention And Problem Solving (Vol 3), By John R. Koza, we make certain that you will not locate bored time.
Correct feels, correct facts, and correct subjects might end up being the reasons of why you review a book. However, to make you feel so completely satisfied, you can take Genetic Programming III: Darwinian Invention And Problem Solving (Vol 3), By John R. Koza as one of the sources. It is actually matched to be the analysis publication for somebody like you, that actually require resources regarding the topic. The topic is actually growing now and obtaining the most recent publication could assist you find the most recent response and facts.
Having this book but never attempting to check out is kind of rubbish. You should read it even few. Reviewing by few is actually better than nothing. You can enjoy reading by beginning in the really enjoyable time. The moment where you can actually filter the information required from this book. The Genetic Programming III: Darwinian Invention And Problem Solving (Vol 3), By John R. Koza will be so helpful when you truly comprehend just what actually this publication uses. So, find your on method to see exactly how your selection concerning the brand-new life within guide.
Review
"Koza, Bennett, Andre, and Keane's evolutionary algorithm builds more complex and useful structures than the other approaches to computer learning that I have seen." —John McCarthy, Stanford University"John Koza and colleagues have demonstrated that genetic programming can be used to search highly discontinuous spaces and thereby find amazing solutions to practical engineering problems."—Bernard Widrow, Stanford University"In this impressive volume, the authors demonstrate that genetic programming is more than an intriguing idea-it is a practical synthesis method for solving hard problems."—Nils J. Nilsson, Stanford University"Through careful experiment, keen algorithmic intuition, and relentless application the authors deliver important results that rival those achieved by human designers. All readers in genetic and evolutionary computation and the related fields of artificial life, agents, and adaptive behavior will want this volume in their collections."—David E. Goldberg, University of Illinois at Urbana-Champaign"John Koza and his coauthors continue their relentless pursuit of a holy grailin computer science: automatic programming."—Moshe Sipper, Swiss Federal Institute of Technology (EPFL), Lausanne
Read more
From the Back Cover
"Koza, Bennett, Andre, and Keane's evolutionary algorithm builds more complex and useful structures than the other approaches to computer learning that I have seen." --John McCarthy, Stanford University"John Koza and colleagues have demonstrated that genetic programming can be used to search highly discontinuous spaces and thereby find amazing solutions to practical engineering problems." --Bernard Widrow, Stanford University"In this impressive volume, the authors demonstrate that genetic programming is more than an intriguing idea-it is a practical synthesis method for solving hard problems."-Nils J. Nilsson, Stanford University"Through careful experiment, keen algorithmic intuition, and relentless application the authors deliver important results that rival those achieved by human designers. All readers in genetic and evolutionary computation and the related fields of artificial life, agents, and adaptive behavior will want this volume in their collections."-David E. Goldberg, University of Illinois at Urbana-Champaign"John Koza and his coauthors continue their relentless pursuit of a holy grailin computer science: automatic programming."-Moshe Sipper, Swiss Federal Institute of Technology (EPFL), LausanneGenetic 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.FeaturesExplains how the success of genetic programming arises from seven fundamental differences distinguishing it from conventional approaches to artificial intelligence and machine learningDescribes how genetic programming uses architecture-altering operations to make on-the-fly decisions on whether to use subroutines, loops, recursions, and memoryDemonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a system for automatically creating computer programsPresents the general-purpose Genetic Programming Problem SolverFocuses 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 moreIntroduces evolvable hardware in the form of field-programmable gate arraysIncludes an introduction to genetic programming for the uninitiated
Read more
See all Editorial Reviews
Product details
Hardcover: 1154 pages
Publisher: Morgan Kaufmann; 1st edition (May 14, 1999)
Language: English
ISBN-10: 1558605436
ISBN-13: 978-1558605435
Product Dimensions:
7.5 x 1.5 x 9.5 inches
Shipping Weight: 4.2 pounds
Average Customer Review:
4.5 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#847,551 in Books (See Top 100 in Books)
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.
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.However most important and intriguing thing about this book is the provocative questions raised concerning definitions and claims of human competitive performance, "Darwinian invention" and artificial intelligence - particularly whether we have already passed an important milestone in the history of AI - automatic programming.
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.
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza PDF
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza EPub
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza Doc
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza iBooks
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza rtf
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza Mobipocket
Genetic Programming III: Darwinian Invention and Problem Solving (Vol 3), by John R. Koza Kindle
0 komentar:
Posting Komentar