Advances in Genetic Programming PDF Download

Author: Kenneth E. Kinnear (Jr.)
Publisher: MIT Press
ISBN: 9780262111881
Category : Computers
Languages : en
Pages : 544

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Book Description
Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in manu of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public-domain code is available, and on how to become part of the active genetic programming community via electronic mail.

Author: Peter J. Angeline
Publisher: Bradford Books
ISBN: 9780262011587
Category : Computers
Languages : en
Pages : 538

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Book Description
Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers.

Author: Kenneth E. Kinnear
Publisher: MIT Press
ISBN: 9780262194235
Category : Computers
Languages : en
Pages : 504

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Book Description
Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in manu of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public-domain code is available, and on how to become part of the active genetic programming community via electronic mail.

Author: Wolfgang Banzhaf
Publisher: Springer Science & Business
ISBN: 9781558605107
Category : Computers
Languages : en
Pages : 506

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Book Description
To order this title for shipment to Austria, Germany, or Switzerland, please contact dpunkt verlag directly. "[The authors] have performed a remarkable double service with this excellent book on genetic programming. First, they give an up-to-date view of the rapidly growing field of automatic creation of computer programs by means of evolution and, second, they bring together their own innovative and formidable work on evolution of assembly language machine code and linear genomes." --John R. Koza Since the early 1990s, genetic programming (GP)-a discipline whose goal is to enable the automatic generation of computer programs-has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks. This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

Author: Una-May O'Reilly
Publisher: Springer Science & Business Media
ISBN: 0387232540
Category : Computers
Languages : en
Pages : 330

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Book Description
The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other's chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the book would be possible. We also thank our keynote speakers Lawrence ("Dave") Davis of NuTech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants.

Author: Amir H. Gandomi
Publisher: Springer
ISBN: 3319208837
Category : Computers
Languages : en
Pages : 589

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Book Description
This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.

Author: Zbigniew Michalewicz
Publisher: Springer Science & Business Media
ISBN: 3662033151
Category : Computers
Languages : en
Pages : 392

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Book Description
Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.

Author:
Publisher: Lulu.com
ISBN: 1409200736
Category : Computers
Languages : en
Pages : 252

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Book Description
Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

Author: W.B. Langdon
Publisher: Springer Science & Business Media
ISBN: 9780792381358
Category : Computers
Languages : en
Pages : 298

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Book Description
Computers that `program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of Genetic Programming, and Data Structures: Genetic Programming + Data Structures = Automatic Programming!. This book is motivated by the observation from software engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognises a Dyck context free language, and implements a simple four function calculator. In these cases, an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.

Author: Melanie Mitchell
Publisher: MIT Press
ISBN: 9780262631853
Category : Computers
Languages : en
Pages : 226

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Book Description
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.