ACM Home Page
Please provide us with feedback. Feedback
Algebraic simplification of GP programs during evolution
Full text PdfPdf (290 KB)
Source Genetic And Evolutionary Computation Conference archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation table of contents
Seattle, Washington, USA
SESSION: Genetic programming: papers table of contents
Pages: 927 - 934  
Year of Publication: 2006
ISBN:1-59593-186-4
Authors
Phillip Wong  Victoria University of Wellington, Wellington, New Zealand
Mengjie Zhang  Victoria University of Wellington, Wellington, New Zealand and M&E College, Agricultural University of Hebei, Baoding, China
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 47,   Citation Count: 4
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1143997.1144156
What is a DOI?

ABSTRACT

Program bloat is a fundamental problem in the field of Genetic Programming (GP). Exponential growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Simplification is an attempt to remove such redundancies from programs. This paper looks at the effects of applying an algebraic simplification algorithm to programs during the GP evolution. The GP system with the simplification is examined and compared to a standard GP system on four regression and classification problems of varying difficulty. The results suggest that the GP system employing a simplification component can achieve superior efficiency and effectiveness to the standard system on these problems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
T. Blickle and L. Thiele. Genetic programming and redundancy. In J. Hopf, editor, Genetic Algorithms within the Framework of Evolutionary Computation, pages 33--38, Germany, 1994.
 
3
 
4
B. Cherowitzo, 2006. Lecture Notes. http://www-math.cudenver.edu/wcherowi/courses/m5410/exeucalg.html. Visited on 7 January 2006.
 
5
 
6
7
 
8
 
9
 
10
 
11
T. Loveard and V. Ciesielski. Representing classification problems in genetic programming. In Proceedings of the Congress on Evolutionary Computation, volume 2, pages 1070--1077, Seoul, Korea, 2001. IEEE Press.
12
 
13
R. Poli. Genetic programming for image analysis. In Proceedings of the First Annual Conference, pages 363--368, Stanford University, CA, USA, 28-31 July 1996. MIT Press.
 
14
T. Soule, J. A. Foster, and J. Dickinson. Code growth in genetic programming. In Proceedings of the First Annual Conference, pages 215--223, Stanford University, CA, USA, 28-31 1996. MIT Press.
 
15
 
16
 
17
M. Zhang and W. Smart. Multiclass object classification using genetic programming. In Applications of Evolutionary Computing, EvoWorkshops2004, volume 3005 of LNCS, pages 369--378, Portugal. 2004. Springer Verlag.


Collaborative Colleagues:
Phillip Wong: colleagues
Mengjie Zhang: colleagues