research-article

Visualisation and Analysis of Genetic Records Produced by Cartesian Genetic Programming

Authors:

Lukas Sekanina,

Vlastimil KapustaAuthors Info & Claims

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Pages 1411 - 1418

https://doi.org/10.1145/2908961.2931740

Published: 20 July 2016 Publication History

Abstract

Cartesian genetic programming (CGP) is a branch of genetic programming in which candidate designs are represented using directed acyclic graphs. Evolutionary circuit design is the most typical application of CGP. This paper presents a new software tool---CGPAnalyzer---developed to analyse and visualise a genetic record (i.e. a log file) generated by CGP-based circuit design software. CGPAnalyzer automatically finds key genetic improvements in the genetic record and presents relevant phenotypes. The comparison module of CGPAnalyzer allows the user to select two phenotypes and compare their structure, history and functionality. It thus enables to reconstruct the process of discovering new circuit designs. This feature is demonstrated by means of the analysis of the genetic record from a 9-parity circuit evolution. The CGPAnalyzer tool is a desktop application with a graphical user interface created using Java v.8 and Swing library.

References

[1]

B. Burlacu, M. Affenzeller, M. Kommenda, S. M. Winkler, and G. Kronberger. Visualization of genetic lineages and inheritance information in genetic programming. In Genetic and Evolutionary Computation Conference, Companion Material Proceedings, pages 1351--1358. ACM, 2013.

Digital Library

[2]

A. Cruz, P. Machado, F. Assunção, and A. Leitão. Elicit: Evolutionary computation visualization. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pages 949--956. ACM, 2015.

Digital Library

[3]

J. M. Daida, A. M. Hilss, D. J. Ward, and S. L. Long. Visualizing tree structures in genetic programming. Genetic Programming and Evolvable Machines, 6(1):79--110, 2005.

Digital Library

[4]

A. Ekart and S. Gustafson. A data structure for improved GP analysis via efficient computation and visualisation of population measures. In 7th European Conference on Genetic Programming, EuroGP 2004, Proceedings, volume 3003 of LNCS, pages 35--46. Springer-Verlag, 2004.

[5]

B. W. Goldman and W. F. Punch. Analysis of cartesian genetic programming's evolutionary mechanisms. IEEE Transactions on Evolutionary Computation, 19(3):359--373, 2015.

Digital Library

[6]

S. M. Gustafson, W. B. Langdon, and J. Koza. Bibliography on genetic programming, 2015. http://liinwww.ira.uka.de/bibliography/Ai/genetic.programming.html.

[7]

P. Kaufmann, C. Plessl, and M. Platzner. EvoCaches: Application-specific Adaptation of Cache Mappings. In Proceedings of the NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pages 11--18. IEEE Computer Society, 2009.

Digital Library

[8]

E. Keedwell, M. Johns, and D. Savic. Spatial and temporal visualisation of evolutionary algorithm decisions in water distribution network optimisation. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pages 941--948. ACM, 2015.

Digital Library

[9]

J. R. Koza. Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, 2003.

Digital Library

[10]

E. Lutton, H. Gilbert, W. Cancino, B. Bach, P. Parrend, and P. Collet. Gridvis: Visualisation of island-based parallel genetic algorithms. In Applications of Evolutionary Computation: 17th European Conference, Evo Applications 2014, pages 702--713. Springer Berlin Heidelberg, 2014.

[11]

J. F. Miller. Cartesian Genetic Programming. Springer-Verlag, 2011.

[12]

J. F. Miller and S. L. Smith. Redundancy and computational efficiency in cartesian genetic programming. IEEE Transactions on Evolutionary Computation, 10(2):167--174, 2006.

Digital Library

[13]

J. F. Miller and P. Thomson. Cartesian Genetic Programming. In Proc. of the 3rd European Conference on Genetic Programming EuroGP2000, volume 1802 of LNCS, pages 121--132. Springer, 2000.

Digital Library

[14]

E. Pedroni. JCGP, 2014. https://bitbucket.org/epedroni/jcgp/.

[15]

T. Routen. Techniques for the visualisation of genetic algorithms. In IEEE World Congress on Computational Intelligence. Proceedings of the First IEEE Conference on Evolutionary Computation, pages 846--851 vol.2. IEEE, 1994.

[16]

E. Stomeo, T. Kalganova, and C. Lambert. Generalized disjunction decomposition for evolvable hardware. IEEE Transaction Systems, Man and Cybernetics, Part B, 36(5):1024--1043, 2006.

Digital Library

[17]

A. J. Turner and J. F. Miller. Introducing a cross platform open source cartesian genetic programming library. Genetic Programming and Evolvable Machines, 16(1):83--91, 2014.

Digital Library

[18]

T. Tusar and B. Filipic. Visualization of pareto front approximations in evolutionary multiobjective optimization: A critical review and the prosection method. IEEE Transactions on Evolutionary Computation, 19(2):225--245, 2015.

Digital Library

[19]

Z. Vasicek. Cartesian GP in optimization of combinational circuits with hundreds of inputs and thousands of gates. In Proceedings of the 18th European Conference on Genetic Programming -- EuroGP, LCNS 9025, pages 139--150. Springer International Publishing, 2015.

[20]

Z. Vasicek and L. Sekanina. Tools4CGP -- tools for cartesian genetic programming, 2008. http://www.fit.vutbr.cz/vasicek/cgp/tools/.

[21]

Z. Vasicek and L. Sekanina. Formal verification of candidate solutions for post-synthesis evolutionary optimization in evolvable hardware. Genetic Programming and Evolvable Machines, 12(3):305--327, 2011.

Digital Library

[22]

Z. Vasicek and L. Sekanina. Evolutionary design of complex approximate combinational circuits. Genetic Programming and Evolvable Machines, 17(2):1--24, 2016.

Digital Library

[23]

Z. Vasicek and K. Slany. Efficient phenotype evaluation in cartesian genetic programming. In Proc. of the 15th European Conference on Genetic Programming, LNCS 7244, pages 266--278. Springer Verlag, 2012.

Digital Library

Cited By

Walter MWalker DCraven MWagner M(2022)An explainable visualisation of the evolutionary search processProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533984(1794-1802)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533984
Medvet EVirgolin MCastelli MBosman PGonçalves ITušar T(2018)Unveiling evolutionary algorithm representation with DU mapsGenetic Programming and Evolvable Machines10.1007/s10710-018-9332-519:3(351-389)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10710-018-9332-5
Medvet ETušar T(2017)The DU mapProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082554(1705-1712)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082554

Index Terms

Visualisation and Analysis of Genetic Records Produced by Cartesian Genetic Programming
1. Human-centered computing
  1. Visualization
    1. Visualization systems and tools
      1. Visualization toolkits

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cover image ACM Conferences

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

July 2016

1510 pages

ISBN:9781450343237

DOI:10.1145/2908961

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 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]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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Vysoké Uení Technické v Brně
Grantová Agentura České Republiky

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;

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

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Cited By

Walter MWalker DCraven MWagner M(2022)An explainable visualisation of the evolutionary search processProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533984(1794-1802)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3533984
Medvet EVirgolin MCastelli MBosman PGonçalves ITušar T(2018)Unveiling evolutionary algorithm representation with DU mapsGenetic Programming and Evolvable Machines10.1007/s10710-018-9332-519:3(351-389)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10710-018-9332-5
Medvet ETušar T(2017)The DU mapProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082554(1705-1712)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082554

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