Genetic Programming and Data Structures

1. Front Matter

   Pages i-xiii

   PDF

2. Introduction

   • W. B. Langdon

   Pages 1-7

3. Genetic Programming — Computers Using “Natural Selection” to Generate Programs

   • W. B. Langdon

   Pages 9-42

4. Advanced Genetic Programming Techniques

   • W. B. Langdon

   Pages 43-59

5. Evolving a Stack

   • W. B. Langdon

   Pages 61-80

6. Evolving a Queue

   • W. B. Langdon

   Pages 81-122

7. Evolving a List

   • W. B. Langdon

   Pages 123-142

8. Problems Solved Using Data Structures

   • W. B. Langdon

   Pages 143-166

9. Evolution of GP Populations

   • W. B. Langdon

   Pages 167-208

10. Conclusions

    • W. B. Langdon

    Pages 209-212

11. Back Matter

    Pages 213-279

    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.

• Scheduling
• algorithms
• artificial intelligence
• automatic programming
• code
• data structure
• data structures
• genetic programming
• network
• software engineering

`The book is nicely produced ... and well illustrated. The text is well written and is easy to read and understand. The concepts in genetic programming are well presented, as are those in genetic algorithms. The references are selected to be useful. People of a non-mathematical disposition will find few problems in reading this book.'
Robotica, 17 (1999)

• The University of Birmingham, UK

  W. B. Langdon