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A graphical user interface for evolutionary algorithms

Author:

Zoltán TóthAuthors Info & Claims

Acta Cybernetica, Volume 16, Issue 2

Pages 337 - 365

Published: 02 January 2003 Publication History

Abstract

The purpose of Generic Evolutionary Algorithms Programming Library (GEA¹) system is to provide researchers with an easy-to-use, widely applicable and extendable programming library which solves real-world optimization problems by means of evolutionary algorithms. It contains algorithms for various evolutionary methods, implemented genetic operators for the most common representation forms for individuals, various selection methods, and examples on how to use and expand the library. All these functions assure that GEA can be effectively applied on many problems. GraphGEA is a graphical user interface to GEA written with the GTK API. The numerous parameters of the evolutionary algorithm can be set in appropriate dialog boxes. The program also checks the correctness of the parameters and saving/restoring of parameter sets is also possible. The selected evolutionary algorithm can be executed interactively on the specified optimization problem through the graphical user interface of GraphGEA, and the results and behavior of the EA can be observed on several selected graphs and drawings. While the main purpose of GEA is solving optimization problems, that of GraphGEA is education and analysis. It can be of great help for students understanding the characteristics of evolutionary algorithms and researchers of the area can use it to analyze an EA's behavior on particular problems.

References

[1]

{1} M. Abramson and L. Hunter. Classification using cultural co-evolution and genetic programming. In J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 249-254, Stanford University, CA, USA, 28-31 July 1996. MIT Press.

[2]

{2} K. Aoki, H. Takagi, and N. Fujimura. Interactive GA-based design support system for lighting design in computer graphics. In International Conference on Soft Computing (IIZUKA'96), pages 533-536, Iizuka, Fukuoka, Japan, 1996. World Scientific.

[3]

{3} W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann.

[4]

{4} P. A. N. Bosman. EA visualizer. http://www.cs.ruu.nl/people/peterb/computer/ ea/eavisualizer/EAVisualizer.htm.

[5]

{5} C. Caldwell and V. S. Johnston. Tracking a criminal suspect through face-space with a genetic algorithm. In ICGA91, pages 416-421, 1991.

[6]

{6} T. D. Collins. The Application of Software Visualization Technology to Evolutionary Computation: A Case Study in Genetic Algorithms. Ph.D thesis, Knowledge Media Institute, The Open University, Milton Keynes, UK, 1998.

[7]

{7} T. Dabs. Eine Entwicklungsumgebung zum Monitoring Genetischer Algorithmen. Master's thesis, University of Würzburg, 1994.

[8]

{8} C. Darwin. On the Origin of Species. Murray, London, 1859.

[9]

{9} L. Davis. Adapting operator probabilities in genetic algorithms. In Proceedings of the Third ICGA, pages 61-67. Morgan Kaufmann, 1989.

[10]

{10} K. A. De Jong. An analysis of the behavior of a class of genetic adaptive systems. Ph.D thesis, University of Michigan, 1975.

[11]

{11} S. Droste. Efficient genetic programming for finding good generalizing boolean functions. In J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 82-87, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann.

[12]

{12} T. Fühner and C. Jacob. Evolvision - an evolvica visualization tool. In L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, hans Michael Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. H. Garzon, and E. Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference, page 176, San Francisco, California, USA, 7-11 July 2001. Morgan Kaufmann.

[13]

{13} D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning . Addison-Wesley Reading, MA, 1989.

[14]

{14} J. Graph and W. Banzhaf. Interactive evolution of images. In International Conference on Evolutionary Programming, 1995.

[15]

{15} J. H. Holland. Adaption of Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan, 1975.

[16]

{16} C. Jacob. Principia Evolvica - Simulierte Evolution mit Mathematica. Dpunkt Verlag, 1997.

[17]

{17} C. Jacob. Principia Evolvica - Simulierte Evolution mit Mathematica, page 443. In {16}, 1997.

[18]

{18} G. Kókai, Z. Tóth, and R. Ványi. Application of genetic algorithms with more populations for Lindenmayer systems. In Proceedings of the International Symposium on Engineering of Intelligent Systems, EIS'98, pages 324-331. ICSC Academic Press, 1998.

[19]

{19} G. Kókai, Z. Tóth, and R. Ványi. Evolving artificial trees described by parametric L-systems. In Proceedings of the First Canadian Workshop on Soft Computing, pages 1722-1728, Edmonton, Alberta, Canada, 9 # may 1999.

[20]

{20} G. Kókai, R. Ványi, and Z. Tóth. Parametric L-system description of the retina with combined evolutionary operators. In Banzhaf et al. {3}, pages 1588-1595.

[21]

{21} J. R. Koza. Evolving a computer program to generate random numbers using the genetic programming paradigm. In R. K. Belew and L. B. Booker, editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 37-44, University of California - San Diego, La Jolla, CA, USA, 13-16 July 1991. Morgan Kaufmann.

[22]

{22} J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, Massachusetts, 1992.

Digital Library

[23]

{23} J. R. Koza. Automated discovery of detectors and iteration-performing calculations to recognize patterns in protein sequences using genetic programming. In Proceedings of the Conference on Computer Vision and Pattern Recognition, pages 684-689. IEEE Computer Society Press, 1994.

[24]

{24} D. Levine, M. Facelllo, and P. Hallstrom. Stalk: An interactive system for virtual molecular docking. IEEE Science and Engineering, 2/97:55-67, 1997.

[25]

{25} Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Artificial Intelligence. Springer-Verlag, 1992.

[26]

{26} J. F. Miller. An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In Banzhaf et al. {3}, pages 1135-1142.

[27]

{27} M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge Massachusetts, 1996.

Digital Library

[28]

{28} U.-M. O'Reilly and G. Ramachandran. A preliminary investigation of evolution as a form design strategy. In C. Adami, R. K. Belew, H. Kitano, and C. E. Taylor, editors, Proceedings of the Sixth International Conference on Artificial Life, University of California, Los Angeles, 26-29 June 1998. MIT Press, Cambridge.

[29]

{29} V. P. Plagianakos and M. N. Vrahatis. Training neural networks with 3-bit integer weights. In Banzhaf et al. {3}, pages 910-915.

[30]

{30} H. Pohlheim. Visualization of evolutionary algorithms - set of standard techniques and multidimensional visualization. In Banzhaf et al. {3}, pages 533-540.

[31]

{31} I. Rechenberg. Evolutionsstrategien: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Fromman-Holzboog, Stuttgart, 1973.

[32]

{32} H.-P. Schwefel. Numerische Optimierung von Computer-Modellen mittels der Evolutonsstrategie. Interdisciplinary Systems research (26), Birkh.user, Basel, 1977.

[33]

{33} J. Sherrah. Automatic Feature Extraction for Pattern Recognition. PhD thesis, University of Adelaide, South Australia, July 1998.

[34]

{34} J. R. Smith. Designing biomorphs with an interactive genetic algorithm. In ICGA91, pages 535-538, 1991.

[35]

{35} Z. Tóth. The Generic Evolutionary Algorithms Programming Library. Master's thesis, University of Szeged, Szeged, Hungary, 2000.

[36]

{36} Z. Tóth and G. Kókai. An evolutionary optimum searching tool. In The Proceedings of the Fourteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE- 2001), volume 2070 of LNAI, pages 19-24, Budapest, Hungary, June 4-7 2001. Springer-Verlag.

[37]

{37} Z. Tóth, G. Kókai, and R. Ványi. Interactive visual tree evolution. In EIS2000 Second International ICSC Symposium on Engineering of Intelligent Systems, June 27-30, 2000 at the University of Paisley, Scotland, U.K., pages 384-390, 2000.

[38]

{38} R. Ványi. Modelling the Evolution of the Flora. Bachelor's thesis (in Hungarian), József Attila University, Szeged, Hungary, 1998.

[39]

{39} R. Ványi, G. Kókai, Z. Tóth, and T, Petö. Grammatical retina description with enhanced methods. In R. Poli, W. Banzhaf, W. B. Langdon, J. F. Miller, P. Nordin, and T. C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP'2000, volume 1802 of LNCS, pages 193-208, Edinburgh, Apr. 15-16 2000. Springer-Verlag.

[40]

{40} G. Venturini, M. Slimane, F. Morin, and J. P. A. de Beauville. On using interactive genetic algorithms for knowledge discovery in databases. In ICGA97, pages 696-703, 1997.

[41]

{41} N. Wirth. What can we do about the unnecessary diversity of notation for syntatic definitions. Communications of the ACM, 20(11):822-823, Nov. 1977.

Digital Library

Cited By

De Lorenzo AMedvet ETušar TBartoli AAuger AStützle T(2019)An analysis of dimensionality reduction techniques for visualizing evolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326868(1864-1872)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326868
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
Show More Cited By

Index Terms

A graphical user interface for evolutionary algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces

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Published In

cover image Acta Cybernetica

Acta Cybernetica Volume 16, Issue 2

January 2003

157 pages

ISSN:0324-721X

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Institute of Informatics, University of Szeged

Szeged, Hungary

Publication History

Published: 02 January 2003

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Cited By

De Lorenzo AMedvet ETušar TBartoli AAuger AStützle T(2019)An analysis of dimensionality reduction techniques for visualizing evolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326868(1864-1872)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326868
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
Morris DSimon IBasu S(2008)Exposing parameters of a trained dynamic model for interactive music creationProceedings of the 23rd national conference on Artificial intelligence - Volume 210.5555/1620163.1620194(784-791)Online publication date: 13-Jul-2008
https://dl.acm.org/doi/10.5555/1620163.1620194

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