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  1. Genetic Programming and Evolvable Machines
  2. Genetic Programming and Evolvable Machines : Volume 10
  3. Genetic Programming and Evolvable Machines : Volume 10, Issue 1, March 2009
  4. An improved representation for evolving programs
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Genetic Programming and Evolvable Machines : Volume 18
Genetic Programming and Evolvable Machines : Volume 17
Genetic Programming and Evolvable Machines : Volume 16
Genetic Programming and Evolvable Machines : Volume 15
Genetic Programming and Evolvable Machines : Volume 14
Genetic Programming and Evolvable Machines : Volume 13
Genetic Programming and Evolvable Machines : Volume 12
Genetic Programming and Evolvable Machines : Volume 11
Genetic Programming and Evolvable Machines : Volume 10
Genetic Programming and Evolvable Machines : Volume 10, Issue 4, December 2009
Genetic Programming and Evolvable Machines : Volume 10, Issue 3, September 2009
Genetic Programming and Evolvable Machines : Volume 10, Issue 2, June 2009
Genetic Programming and Evolvable Machines : Volume 10, Issue 1, March 2009
Editorial introduction
Acknowledgment
Scaling of program functionality
An improved representation for evolving programs
Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming
Husbands, Holland, and Wheeler (eds): Review of the book “The Mechanical Mind in History”
Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music
Genetic Programming and Evolvable Machines : Volume 9
Genetic Programming and Evolvable Machines : Volume 8
Genetic Programming and Evolvable Machines : Volume 7
Genetic Programming and Evolvable Machines : Volume 6
Genetic Programming and Evolvable Machines : Volume 5
Genetic Programming and Evolvable Machines : Volume 4
Genetic Programming and Evolvable Machines : Volume 3
Genetic Programming and Evolvable Machines : Volume 2
Genetic Programming and Evolvable Machines : Volume 1

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An improved representation for evolving programs

Content Provider SpringerLink
Author Withall, M. S. Hinde, C. J. Stone, R. G.
Copyright Year 2008
Abstract A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining their advantages. This combines the easy reproduction of the linear representation with the inheritable characteristics of the tree representation by using fixed-length blocks of genes representing single program statements. This means that each block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions was evolved as an application of the new Genetic Program components. These functions have the common feature that they all need to be 100% correct to be useful. Traditional Genetic Programming problems have mainly been optimization or approximation problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic increase in the required evolution time.
Starting Page 37
Ending Page 70
Page Count 34
File Format PDF
ISSN 13892576
Journal Genetic Programming and Evolvable Machines
Volume Number 10
Issue Number 1
e-ISSN 15737632
Language English
Publisher Springer US
Publisher Date 2008-08-21
Publisher Place Boston
Access Restriction Subscribed
Subject Keyword Genetic Algorithms Genetic Programming Representation Biomedical Engineering Programming Languages, Compilers, Interpreters Programming Techniques Software Engineering/Programming and Operating Systems Artificial Intelligence (incl. Robotics) Electrical Engineering
Content Type Text
Resource Type Article
Subject Theoretical Computer Science Hardware and Architecture Computer Science Applications Software
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