Article

Investigating the performance of module acquisition in cartesian genetic programming

Authors:

James Alfred Walker,

Julian Francis MillerAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 1649 - 1656

https://doi.org/10.1145/1068009.1068287

Published: 25 June 2005 Publication History

Abstract

Embedded Cartesian Genetic Programming (ECGP) is a form of the graph based Cartesian Genetic Programming (CGP) in which modules are automatically acquired and evolved. In this paper we compare the efficiencies of the ECGP and CGP techniques on three classes of problem: digital adders, digital multipliers and digital comparators. We show that in most cases ECGP shows a substantial improvement in performance over CGP and that the computational speedup is more pronounced on larger problems.

References

[1]

Angeline, P. J. Pollack, J. (1993) Evolutionary Module Acquisition, Proceedings of the 2nd Annual Conference on Evolutionary Programming, pp. 154--163, MIT Press, Cambridge.]]

[2]

Dessi, A. Giani, A. Starita, A. (1999) An Analysis of Automatic Subroutine Discovery in Genetic Programming, GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 996--1001, Morgan-Kaufmann, San Francisco.]]

[3]

Koza, J. R. (1993) Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, London.]]

Digital Library

[4]

Koza, J. R. (1994) Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, London.]]

Digital Library

[5]

Miller, J. F., Thomson, P., and Fogarty T. C. (1997) Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study, Genetic Algorithms and Evolution Strategies in Engineering and Computer Science: Recent Advancements and Industrial Applications. Editors: D. Quagliarella, J. Periaux, C. Poloni and G. Winter, Wiley.]]

[6]

Miller, J. F. (1999) An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach, GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, pp 1135--1142, Morgan Kaufmann, San Francisco.]]

[7]

Miller, J. F. Thomson, P. (2000) Cartesian Genetic Programming, Proceedings of the 3rd European Conference on Genetic Programming, Edinburgh, Lecture Notes in Computer Science, Vol. 1802, pp 121--132, Springer-Verlag, Berlin.]]

Digital Library

[8]

Poli, R. (1996), Parallel Distributed Genetic Programming, Technical Report, CSRP-96-15, University of Birmingham, UK.]]

[9]

Rosca, J. P. (1995) Genetic Programming Exploratory Power and the Discovery of Functions, Proceedings of the 4th Annual Conference of Evolutionary Programming, San Diego, pp 719--736, MIT Press, Cambridge.]]

[10]

Spector, L. (1996) Simultaneous evolution of programs and their control structures, Advances in Genetic Programming II, pp. 137--154, MIT Press, Cambridge.]]

Digital Library

[11]

Spector, L. (2001) Autoconstructive Evolution: Push, PushGP, and Pushpop, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 137--146. San Francisco, CA: Morgan Kaufmann Publishers]]

[12]

Van Belle, T, and Ackley, D.H. (2001) Code Factoring and the Evolution of Evolvability, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 1383--1390. San Francisco, CA: Morgan Kaufmann Publishers]]

[13]

Vassilev, V. K. and Miller J. F. (2000) The Advantages of Landscape Neutrality in Digital Circuit Evolution, Proceedings of the 3rd International Conference on Evolvable Systems: From Biology to Hardware (ICES2000), Lecture Notes in Computer Science, Vol. 1801, 252--263. Springer, Berlin.]]

Digital Library

[14]

Vassilev, V. K. and Miller J. F. (2000) Scalability Problems of Digital Circuit Evolution, 2nd NASA/DOD Workshop on Evolvable Hardware, IEEE Computer Society Press, pp. 55--64.]]

Digital Library

[15]

Walker, J. A. Miller, J. F. (2004) Evolution and Acquisition of Modules in Cartesian Genetic Programming, Proc. of the 7th European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 3003, pp 187--197, Springer-Verlag, Berlin.]]

[16]

Woodward, J. R. (2003) Modularity in Genetic Programming, Proceedings of the Fifth European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 2610, pp. 258--267, Springer-Verlag, Berlin.]]

Digital Library

[17]

Yu, T. and Miller, J. F. (2001) Neutrality and the Evolvability of Boolean Function Landscape, Proceedings of the 4th European Conference on Genetic Programming, Lecture Notes in Computer Science, Vol. 2038, pp. 204--217, Springer-Verlag, Berlin.]]

Digital Library

Cited By

Manazir ARaza K(2019)Recent Developments in Cartesian Genetic Programming and its VariantsACM Computing Surveys10.1145/327551851:6(1-29)Online publication date: 28-Jan-2019
https://dl.acm.org/doi/10.1145/3275518
da Silva JBernardino H(2018)Cartesian Genetic Programming with Crossover for Designing Combinational Logic Circuits2018 7th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2018.00033(145-150)Online publication date: Oct-2018
https://doi.org/10.1109/BRACIS.2018.00033
Tao YZhang LZhang Y(2016)A projection-based decomposition for the scalability of evolvable hardwareSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1636-220:6(2205-2218)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s00500-015-1636-2
Show More Cited By

Recommendations

Cartesian genetic programming
GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Cartesian genetic programming
GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Cartesian Genetic Programming is a form of genetic programming. It is increasing in popularity. It was developed by Julian Miller with Peter Thomson in 1997. In its classic form it uses a very simple integer based genetic representation of a program in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

GECCO05

Sponsor:

GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

Washington DC, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

39
Total Citations
View Citations
316
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Manazir ARaza K(2019)Recent Developments in Cartesian Genetic Programming and its VariantsACM Computing Surveys10.1145/327551851:6(1-29)Online publication date: 28-Jan-2019
https://dl.acm.org/doi/10.1145/3275518
da Silva JBernardino H(2018)Cartesian Genetic Programming with Crossover for Designing Combinational Logic Circuits2018 7th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2018.00033(145-150)Online publication date: Oct-2018
https://doi.org/10.1109/BRACIS.2018.00033
Tao YZhang LZhang Y(2016)A projection-based decomposition for the scalability of evolvable hardwareSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1636-220:6(2205-2218)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1007/s00500-015-1636-2
Miller JTurner AEsparcia-Alcázar A(2015)Cartesian Genetic ProgrammingProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2756571(179-198)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2756571
Pawlak TWieloch BKrawiec K(2015)Semantic Backpropagation for Designing Search Operators in Genetic ProgrammingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.232125919:3(326-340)Online publication date: Jun-2015
https://doi.org/10.1109/TEVC.2014.2321259
Pawlak TWieloch BKrawiec K(2015)Review and comparative analysis of geometric semantic crossoversGenetic Programming and Evolvable Machines10.1007/s10710-014-9239-816:3(351-386)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s10710-014-9239-8
Lopes RCosta E(2014)Developments on the Regulatory Network Computational DeviceInternational Journal of Natural Computing Research10.4018/ijncr.20141001044:4(55-91)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.4018/ijncr.2014100104
Tao YZhang YZhang LGu CArnold D(2014)A projection-based decomposition in EHW method for design of relatively large circuitsProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2598411(153-154)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2598411
Pawlak T(2014)Combining Semantically-Effective and Geometric Crossover Operators for Genetic ProgrammingParallel Problem Solving from Nature – PPSN XIII10.1007/978-3-319-10762-2_45(454-464)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10762-2_45
Miller JAlba E(2013)GECCO 2013 tutorialProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2464578(715-740)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2464578
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents