Genetic Programming and Data Structures : Genetic Programming + Data Structures = Automatic Programming!

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, recognis...

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Main Author: Langdon, William B.
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY Springer US 1998, 1998
Edition:1st ed. 1998
Series:Genetic Programming
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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245 0 0 |a Genetic Programming and Data Structures  |h Elektronische Ressource  |b Genetic Programming + Data Structures = Automatic Programming!  |c by William B. Langdon 
250 |a 1st ed. 1998 
260 |a New York, NY  |b Springer US  |c 1998, 1998 
300 |a XIII, 279 p  |b online resource 
505 0 |a A–Number of Fitness Evaluations Required -- B–Glossary -- C–Scheduling Planned Maintenance of the National Grid -- C.1 Introduction -- C.2 The Electricity Transmission Network in Great Britain -- C.3 The South Wales Region of the UK Electricity Network -- C.4 Approximating Replacement Generation Costs -- C.5 The Fitness Function -- C.6 The Chromosome -- C.7 Greedy Optimisers -- C.8 South Wales Problem without Contingencies -- C.9 South Wales and Genetic Programming -- C.10 South Wales Problem with Contingencies -- C.11 Conclusions -- C.12 Future Work -- C.13 Using QGAME -- D–Implementation -- D.1 GP-QUICK -- D.2 Coding Changes to GP-QUICK-2.1 -- D.3 Default Parameters -- D.4 Network Running -- D.5 Reusing Ancestors Fitness Information -- D.6 Caches -- D.7 Compressing the Check Point File -- D.8 Benchmarks -- D.9 Code 
505 0 |a Building Blocks and Introns -- 5.12 Summary -- 6. Evolving a List -- 6.1 Problem Statement -- 6.2 Architecture -- 6.3 Automatically Defined Functions -- 6.4 Choice of Primitives -- 6.5 Fitness Function -- 6.6 Directed Crossover -- 6.7 Parameters -- 6.8 Results -- 6.9 Software Maintenance -- 6.10 Discussion -- 6.11 Conclusions -- 7. Problems Solved Using Data Structures -- 7.1 Balanced Bracket Problem -- 7.2 Dyck Language -- 7.3 Evaluating Reverse Polish Expressions -- 7.4 Work by Others on Solving Problems with Memory -- 7.5 Summary -- 8. Evolution of GP Populations -- 8.1 Price’s Selection and Covariance Theorem -- 8.2 Fisher’s Fundamental Theorem of Natural Selection -- 8.3 Evolution of Stack Problem Populations -- 8.4 Loss of Variety -- 8.5 Measurements of GP Crossover’s Effects -- 8.6 Discussion -- 8.7 Summary -- 9. Conclusions -- 9.1 Recommendations -- 9.2 Future work -- References -- Appendices --  
505 0 |a 1. Introduction -- 1.1 What is Genetic Programming? -- 1.2 Motivation -- 1.3 Outline -- 2. Survey -- 2.1 Introduction -- 2.2 Genetic Algorithms -- 2.3 Genetic Programming -- 2.4 GP Research -- 2.5 GP Applications -- 2.6 Conclusions -- 3. Advanced Genetic Programming Techniques -- 3.1 Background -- 3.2 Tournament Selection -- 3.3 Steady State Populations -- 3.4 Indexed memory -- 3.5 Scalar Memory -- 3.6 Multi-tree programs -- 3.7 Directed Crossover -- 3.8 Demes -- 3.9 Pareto Optimality -- 3.10 Conclusions -- 4. Evolving a Stack -- 4.1 Problem Statement -- 4.2 Architecture -- 4.3 Choice of Primitives -- 4.4 Fitness Function -- 4.5 Parameters -- 4.6 Results -- 4.7 Summary -- 5. Evolving a Queue -- 5.1 Problem Statement -- 5.2 Architecture -- 5.3 Choice of Primitives -- 5.4 Fitness Functions -- 5.5 Parameters -- 5.6 Automatically Defined Functions -- 5.7 Evolved Solutions — Caterpillar -- 5.8 Evolved Programs — Shuffler -- 5.9 Circular Buffer — Given Modulus Increment --  
653 |a Theory of Computation 
653 |a Processor Architectures 
653 |a Computers 
653 |a Data structures (Computer science) 
653 |a Microprocessors 
653 |a Data Structures 
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082 0 |a 005.73 
520 |a 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.  
520 |a 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.  
520 |a 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