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

Genetic Programming and Data Structures Genetic Programming + Data Structures = Automatic Programming! / by William B. Langdon.

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 be...

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Bibliographic Details
Main Author: Langdon, William B. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 1998.
Edition:1st ed. 1998.
Series:Genetic Programming, 1
Springer eBook Collection.
Subjects:
Data structures (Computer science).
Computers.
Microprocessors.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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
  • 5.10 Circular Buffer — Evolving Modulus Increment
  • 5.11 Discussion: 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
  • 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.

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