Part of the book series: Genetic Programming (GPEM, volume 1)
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Table of contents (9 chapters)
About this book
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.
- artificial intelligence
- automatic programming
- data structure
- data structures
- genetic programming
- software engineering
Robotica, 17 (1999)
Authors and Affiliations
The University of Birmingham, UK
W. B. Langdon
Book Title: Genetic Programming and Data Structures
Book Subtitle: Genetic Programming + Data Structures = Automatic Programming!
Authors: W. B. Langdon
Series Title: Genetic Programming
Publisher: Springer New York, NY
eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1998
Hardcover ISBN: 978-0-7923-8135-8Published: 30 April 1998
Softcover ISBN: 978-1-4613-7625-5Published: 29 October 2012
eBook ISBN: 978-1-4615-5731-9Published: 06 December 2012
Series ISSN: 1566-7863
Edition Number: 1
Number of Pages: XIII, 279
Topics: Data Science, Theory of Computation, Processor Architectures