Genetic Programming And Data Structures

Genetic Programming And Data Structures Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Genetic Programming And Data Structures book. This book definitely worth reading, it is an incredibly well-written.

Genetic Programming and Data Structures

W.B. Langdon
Genetic Programming and Data Structures Book PDF

Author : W.B. Langdon
Publisher : Springer Science & Business Media
Page : 298 pages
File Size : 49,7 Mb
Release : 1998-04-30
Category : Computers
ISBN : 0792381351

Get Book

Genetic Programming and Data Structures by W.B. Langdon Pdf

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.

Genetic Programming and Data Structures

William B. Langdon
Genetic Programming and Data Structures Book PDF

Author : William B. Langdon
Publisher : Springer Science & Business Media
Page : 288 pages
File Size : 40,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461557319

Get Book

Genetic Programming and Data Structures by William B. Langdon Pdf

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.

Foundations of Genetic Programming

William B. Langdon,Riccardo Poli
Foundations of Genetic Programming Book PDF

Author : William B. Langdon,Riccardo Poli
Publisher : Springer Science & Business Media
Page : 265 pages
File Size : 46,7 Mb
Release : 2013-03-09
Category : Computers
ISBN : 9783662047262

Get Book

Foundations of Genetic Programming by William B. Langdon,Riccardo Poli Pdf

This is one of the only books to provide a complete and coherent review of the theory of genetic programming (GP). In doing so, it 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 Algorithms + Data Structures = Evolution Programs

Zbigniew Michalewicz
Genetic Algorithms Data Structures Evolution Programs Book PDF

Author : Zbigniew Michalewicz
Publisher : Springer Science & Business Media
Page : 257 pages
File Size : 51,5 Mb
Release : 2013-06-29
Category : Mathematics
ISBN : 9783662028308

Get Book

Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz Pdf

'What does your Master teach?' asked a visitor. 'Nothing,' said the disciple. 'Then why does he give discourses?' 'He only points the way - he teaches nothing.' Anthony de Mello, One Minute Wisdom During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The emergence of massively par allel computers made these algorithms of practical interest. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies, simulated annealing, classifier systems, and neural net works. Recently (1-3 October 1990) the University of Dortmund, Germany, hosted the First Workshop on Parallel Problem Solving from Nature [164]. This book discusses a subclass of these algorithms - those which are based on the principle of evolution (survival of the fittest). In such algorithms a popu lation of individuals (potential solutions) undergoes a sequence of unary (muta tion type) and higher order (crossover type) transformations. These individuals strive for survival: a selection scheme, biased towards fitter individuals, selects the next generation. After some number of generations, the program converges - the best individual hopefully represents the optimum solution. There are many different algorithms in this category. To underline the sim ilarities between them we use the common term "evolution programs" .

Genetic Algorithms + Data Structures = Evolution Programs

Zbigniew Michalewicz
Genetic Algorithms Data Structures Evolution Programs Book PDF

Author : Zbigniew Michalewicz
Publisher : Springer Science & Business Media
Page : 392 pages
File Size : 41,8 Mb
Release : 2013-03-09
Category : Computers
ISBN : 9783662033159

Get Book

Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz Pdf

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.

Genetic Programming and Data Structures

W.B. Langdon
Genetic Programming and Data Structures Book PDF

Author : W.B. Langdon
Publisher : Springer
Page : 279 pages
File Size : 48,6 Mb
Release : 2012-10-29
Category : Computers
ISBN : 1461376254

Get Book

Genetic Programming and Data Structures by W.B. Langdon Pdf

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.

Genetic Algorithms and Genetic Programming

Michael Affenzeller,Stefan Wagner,Stephan Winkler,Andreas Beham
Genetic Algorithms and Genetic Programming Book PDF

Author : Michael Affenzeller,Stefan Wagner,Stephan Winkler,Andreas Beham
Publisher : CRC Press
Page : 395 pages
File Size : 45,5 Mb
Release : 2009-04-09
Category : Computers
ISBN : 9781420011326

Get Book

Genetic Algorithms and Genetic Programming by Michael Affenzeller,Stefan Wagner,Stephan Winkler,Andreas Beham Pdf

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 al

Genetic Programming Theory and Practice

Rick Riolo,Bill Worzel
Genetic Programming Theory and Practice Book PDF

Author : Rick Riolo,Bill Worzel
Publisher : Springer Science & Business Media
Page : 346 pages
File Size : 42,7 Mb
Release : 2003-11-30
Category : Computers
ISBN : 1402075812

Get Book

Genetic Programming Theory and Practice by Rick Riolo,Bill Worzel Pdf

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.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Thomas Duriez,Steven L. Brunton,Bernd R. Noack
Machine Learning Control Taming Nonlinear Dynamics and Turbulence Book PDF

Author : Thomas Duriez,Steven L. Brunton,Bernd R. Noack
Publisher : Springer
Page : 211 pages
File Size : 49,7 Mb
Release : 2016-11-02
Category : Technology & Engineering
ISBN : 9783319406244

Get Book

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence by Thomas Duriez,Steven L. Brunton,Bernd R. Noack Pdf

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.

Advances in Genetic Programming

Kenneth E. Kinnear (Jr.),Peter J. Angeline
Advances in Genetic Programming Book PDF

Author : Kenneth E. Kinnear (Jr.),Peter J. Angeline
Publisher : MIT Press
Page : 544 pages
File Size : 50,9 Mb
Release : 1994
Category : Computers
ISBN : 0262111888

Get Book

Advances in Genetic Programming by Kenneth E. Kinnear (Jr.),Peter J. Angeline Pdf

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 Theory and Practice

Rick Riolo,Bill Worzel
Genetic Programming Theory and Practice Book PDF

Author : Rick Riolo,Bill Worzel
Publisher : Springer Science & Business Media
Page : 317 pages
File Size : 51,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781441989833

Get Book

Genetic Programming Theory and Practice by Rick Riolo,Bill Worzel Pdf

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.

Data Mining Using Grammar Based Genetic Programming and Applications

Man Leung Wong,Kwong Sak Leung
Data Mining Using Grammar Based Genetic Programming and Applications Book PDF

Author : Man Leung Wong,Kwong Sak Leung
Publisher : Springer Science & Business Media
Page : 214 pages
File Size : 51,6 Mb
Release : 2006-04-18
Category : Computers
ISBN : 9780306470127

Get Book

Data Mining Using Grammar Based Genetic Programming and Applications by Man Leung Wong,Kwong Sak Leung Pdf

Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced. A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly. Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.

A Field Guide to Genetic Programming

Anonim
A Field Guide to Genetic Programming Book PDF

Author : Anonim
Publisher : Lulu.com
Page : 252 pages
File Size : 46,6 Mb
Release : 2008
Category : Computers
ISBN : 9781409200734

Get Book

A Field Guide to Genetic Programming by Anonim Pdf

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.

Cartesian Genetic Programming

Julian F. Miller
Cartesian Genetic Programming Book PDF

Author : Julian F. Miller
Publisher : Springer Science & Business Media
Page : 346 pages
File Size : 42,6 Mb
Release : 2011-09-18
Category : Computers
ISBN : 9783642173103

Get Book

Cartesian Genetic Programming by Julian F. Miller Pdf

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.

Advanced Algorithms and Data Structures

Marcello La Rocca
Advanced Algorithms and Data Structures Book PDF

Author : Marcello La Rocca
Publisher : Simon and Schuster
Page : 768 pages
File Size : 43,6 Mb
Release : 2021-08-10
Category : Computers
ISBN : 9781638350224

Get Book

Advanced Algorithms and Data Structures by Marcello La Rocca Pdf

Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization