Genetic Programming

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 ...

Genetic Programming

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 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.

Advances in Genetic Programming

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 ...

Advances in Genetic Programming

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.

A Field Guide to Genetic Programming

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.

A Field Guide to Genetic Programming

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.

Genetic Programming

This book constitutes the refereed proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, held in Essex, UK in April 2003. The 45 revised papers presented were carefully reviewed and selected from 61 submissions.

Genetic Programming

This book constitutes the refereed proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, held in Essex, UK in April 2003. The 45 revised papers presented were carefully reviewed and selected from 61 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from foundational, theoretical, and methodological issues to advanced applications in various fields.

Genetic Programming

Genetic Programming

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.

Genetic Algorithms and Genetic Programming

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP).

Genetic Algorithms and Genetic Programming

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development. The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems. Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.

Foundations of Genetic Programming

This book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to ...

Foundations of Genetic Programming

Genetic programming (GP), one of the most advanced forms of evolutionary computation, has been highly successful as a technique for getting computers to automatically solve problems without having to tell them explicitly how. Since its inceptions more than ten years ago, GP has been used to solve practical problems in a variety of application fields. Along with this ad-hoc engineering approaches interest increased in how and why GP works. This book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.

Genetic Programming III

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.

Genetic Programming III

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

Cartesian Genetic Programming

This book contains chapters written by the leading figures in the development and application of CGP, and it will be essential reading for researchers in genetic programming and for engineers and scientists solving applications using these ...

Cartesian Genetic Programming

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype–phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP. It has been applied to many problems in both computer science and applied sciences. This book contains chapters written by the leading figures in the development and application of CGP, and it will be essential reading for researchers in genetic programming and for engineers and scientists solving applications using these techniques. It will also be useful for advanced undergraduates and postgraduates seeking to understand and utilize a highly efficient form of genetic programming.

Genetic Algorithms and Genetic Programming in Computational Finance

Finally, a menu-driven software program, Simple GP, accompanies the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work ...

Genetic Algorithms and Genetic Programming in Computational Finance

After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this subject. Chapters cover various areas of computational finance, including financial forecasting, trading strategies development, cash flow management, option pricing, portfolio management, volatility modeling, arbitraging, and agent-based simulations of artificial stock markets. Two tutorial chapters are also included to help readers quickly grasp the essence of these tools. Finally, a menu-driven software program, Simple GP, accompanies the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work.

Genetic Programming

A Comparison of Cartesian Genetic Programming and Linear Genetic
Programming Garnett Wilson1,2 and Wolfgang Banzhaf1 1 Memorial Univeristy
of Newfoundland, St. John's, NL, Canada 2 Verafin, Inc., St. John's, NL, Canada {
gwilson ...

Genetic Programming

This book constitutes the refereed proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008, held in Naples, Italy, in March 2008 colocated with EvoCOP 2008. The 21 revised plenary papers and 10 revised poster papers were carefully reviewed and selected from a total of 61 submissions. A great variety of topics are presented reflecting the current state of research in the field of genetic programming, including the latest work on representations, theory, operators and analysis, evolvable hardware, agents and numerous applications.

Advances in Genetic Programming

Clearly, to obtain such a solution, we must abandon the 'outer loop' of the genetic
programming system which requires us to evaluate several generations of
thousands of individuals in order to obtain an advancement in fitness (as
manifested ...

Advances in Genetic Programming

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.

Genetic Programming

Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued
Function Regression Quang Uy Nguyen1, Xuan Hoai Nguyen2, and Michael O'
Neill1 1 Natural Computing Research & Applications Group, University College ...

Genetic Programming

The 12th European Conference on Genetic Programming, EuroGP 2009, took place in Tu ¨bingen, Germany during April 15–17 at one of the oldest univer- ties in Germany, the Eberhard Karls Universitat ¨ Tubing ¨ en. This volume c- tains manuscripts of the 21 oral presentations held during the day, and the nine posters that were presented during a dedicated evening session and reception. The topics covered in this volume re?ectthecurrentstateoftheartofgenetic programming, including representations, theory, operators and analysis, feature selection, generalization, coevolution, and numerous applications. A rigorous, double-blind peer-review process was used, with each submission reviewed by at least three members of the international ProgramCommittee. In total, 57 papers were submitted with an acceptance rate of 36% for full papers and an overall acceptance rate of 52% including posters. The MyReview m- agement software originally developed by Philippe Rigaux, Bertrand Chardon, and other colleagues from the Universit´ e Paris-Sud Orsay, France was used for the reviewing process. We are sincerely grateful to Marc Schoenauer from IN- RIA, France for his continued assistance in hosting and managing the software. Paper review assigments were largely done by an optimization process matching paper keywords to keywords of expertise submitted by reviewers.

Genetic Programming IV

The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered ...

Genetic Programming IV

Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law

Linear Genetic Programming

In this chapter a comparison between the linear representation and the traditional
tree representation of genetic programming is performed. The comparison
examines prediction performance and model size based on two collections of ...

Linear Genetic Programming

Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it includes sufficient introductory material for students and newcomers to the field.

Genetic Programming

We introduce a genetic programming (GP) approach for evolving genetic
networks that demonstrate desired dynamics when simulated as a discrete
stochastic process. Our representation of genetic networks is based on a
biochemical ...

Genetic Programming

The present volume contains the contributions for the 9th European Conference on Genetic Programming (EuroGP 2006). The conference took place during April 10-12, 2006 in Budapest, Hungary. EuroGP is a well-established conf- ence and the only one exclusively devoted to genetic programming worldwide. EuroGP began as a workshopin 1998 in Paris, and has been held annually since then,becomingaconferenceinEdinburghin2000.Allpreviousproceedingshave been published by Springer in the Lecture Notes in Computer Science series. More recently, EuroGP has been co-located with EvoCOP 2006, the 6th Eu- pean Conference on Evolutionary Computation in Combinatorial Optimization, and the EvoWorkshops, focusing on applications of evolutionary computation, resulting in the largest combined event dedicated to evolutionary computation in Europe. Genetic programming (GP) is evolutionary computation that solves complex problems or tasks by evolving and adapting a population of computer programs, using Darwinian evolution and Mendelian genetics as its sources of inspiration. The 32 papers included in these proceedings address fundamental and theore- cal issues, along with a wide variety of papers dealing with di'erent application areas,suchascomputerscience,engineering,machinelearning,Kolmogorovc- plexity, biology and computational design, showing that GP is a powerful and practical problem-solving paradigm. A rigorous, double-blind, selection mechanism was applied to 59 submitted papers,thatresultedintheacceptanceof21plenarytalks(36%acceptancerate) and 11 poster presentations (54% global acceptance rate for talks and posters).

Genetic Programming

Analytic Solutions to Differential Equations under Graph-Based Genetic
Programming Tom Seaton1, Gavin Brown2, and Julian F. Miller1 1 Department of
Electronics, University of York 2 School of Computer Science, University of
Manchester ...

Genetic Programming

rangefromsolvingdi?erentialequations,routingproblems to ?le type detection, object-oriented testing, agents. This year we received 48 submissions, of which 47 were sent to the reviewers.

Genetic Programming

Genetic. Programming. Bloat. with. Dynamic. Fitness. W. B. Langdon and R. Poli
School of Computer Science, University ... We conclude genetic programming,
when evolving artificial ant control programs, is surprisingly little effected by large
 ...

Genetic Programming

This book presents the latest in mammary gland transgenesis, the exploitation of transgenic technology for the production of therapeutic proteins by routine or conventional methods. Following a section with an overview of all relevant methodologies, readers will find relevant information on the regulation of milk gene expression and bioreactor species such as cattle, rabbits and pigs.

Genetic Programming

The aim of this paper is to prove the effectiveness of the genetic programming
approach in automatic parsing of sentences of real texts. Classical parsing
methods are based on complete search techniques to find the different
interpretations of ...

Genetic Programming

This book constitutes the refereed proceedings of the 7th European Conference on Genetic Programming, EuroGP 2004, held in Coimbra, Portugal, in April 2004. The 38 revised papers presented were carefully reviewed and selected from 61 submissions. The papers deal with a variety of foundational and methodological issues as well as with advanced applications in areas like engineering, computer science, language understanding, bioinformatics, and design.

Genetic Programming Theory and Practice

The most outstanding evidence of pressure towards stability is the phenomenon
of code growth ( or bloat ) in genetic programming ( GP ) ( Koza , 1992 ; Blickle
and Thiele , 1994 ; Nordin and Banzhaf , 1995 ; McPhee and Miller , 1995 ; Soule
 ...

Genetic Programming Theory and Practice

Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory. The book also includes chapters on the dynamics of GP, the selection of operators and population sizing, specific applications such as stock selection in emerging markets, predicting oil field production, modeling chemical production processes, and developing new diagnostics from genomic data. Genetic Programming Theory and Practice is an excellent reference for researchers working in evolutionary algorithms and for practitioners seeking innovative methods to solve difficult computing problems.