New Generation Computing

, Volume 23, Issue 2, pp 129–142 | Cite as

Reference chromosome to overcome user fatigue in IEC

  • Yago Saez
  • Pedro Isasi
  • Javier Segovia
  • Julio C. Hernandez
Special Issue


Evolutionary Computation encompasses computational models that follow a biological evolution metaphor. The success of these techniques is based on the maintenance of the genetic diversity, for which it is necessary to work with large populations. However, it is not always possible to deal with such large populations, for instance, when the adequacy values must be estimated by a human being (Interactive Evolutionary Computation, IEC). This work introduces a new algorithm which is able to perform very well with a very low number of individuals (micropopulations) which speeds up the convergence and it is solving problems with complex evaluation functions. The new algorithm is compared with the canonical genetic algorithm in order to validate its efficiency. Two experimental frameworks have been chosen: table and logotype designs. An objective evaluation measures has been proposed to avoid user interaction in the experiments. In both cases the results show the efficiency of the new algorithm in terms of quality of solutions and convergence speed, two key issues in decreasing user fatigue.


Interactive Evolutionary Computation Genetic Algorithm Micropopulations Chromosome Appearance Probability Matrix Fatigue Design Table Logotype 


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  1. 1).
    Goldberg, D.E., “Genetic Algorithms in Search, Optimization, and Machine Learning,”Ed. Addison-Wesley, Reading, MA., 1989.MATHGoogle Scholar
  2. 2).
    Grefenstette, J.J., “Optimization of Control Parameters for Generic Algorithms,”IEEE Transactions on Systems, Man, and Cybernetics, 16, 1, pp. 122–128, 1986.CrossRefGoogle Scholar
  3. 3).
    Goldberg, D.E. and Rundnick, M., “Genetic Algorithms and the Variance of Fitness,”Complex Systems, 5, 3, pp. 265–278, 1991.MATHGoogle Scholar
  4. 4).
    Caldwell, C. and Johnston, V.S., “Tracking a Criminal Suspect through Face Space with a Genetic Algorithm,”ICGA-4, pp. 416–421, 1991.Google Scholar
  5. 5).
    Goldberg, D.E., Deb, K. and Clark, J.H., “Genetic Algorithms, Noise, and the Sizing of Populations,”Complex Syst., 6, 4, pp. 333–362, 1992.MATHGoogle Scholar
  6. 6).
    Harik, G., Cantu-Paz, E., Goldberg, D.E. and Miller, B.L., “The Gambler’s Ruin Problem, Genetic Algorithms, and the Sizing of Populations,”Transactions on Evolutionary Computation, 7, pp. 231–253, 1999.CrossRefGoogle Scholar
  7. 7).
    He, J. and Yao, X., “From an Individual to a Population: An Analysis of the First Hitting Time of Population-based Evolutionary Algorithms,”IEEE Transactions on Evolutionary Computation, 6, pp. 495–511, Oct., 2002.CrossRefGoogle Scholar
  8. 8).
    Bentley, P., “From Coffee Tables to Hospitals: Generic Evolutionary Design,”Evolutionary design by computers, Morgan-Kauffman, pp. 405–423, 1999.Google Scholar
  9. 9).
    Sims, K., “Artificial Evolution for Computer Graphics,”Comp. Graphics, 25, 4, pp. 319–328, 1991.CrossRefMathSciNetGoogle Scholar
  10. 10).
    Moore, J.H., “GAMusic: Genetic Algorithm to Evolve Musical Melodies,”Windows 3.1 Software available in:, 1994.Google Scholar
  11. 11).
    Baluja, S., Pomerleau, D. and Jochem, T., “Towards Automated Artificial Evolution for Computer-generated Images,”Connection Science, pp. 325–354, 1994.Google Scholar
  12. 12).
    Biles, P., Anderson G. and Loggi, L.W., “Neural Network Fitness Functions for a Musical IGA,” inProceedings of IIA96/SOCO96. International ICSC Symposia on Intelligent Industrial Automation and Soft Computing., 1996.Google Scholar
  13. 13).
    Gonzalez, C., Lozano, J. and Larranarraga, P., “Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems,”Complex Systems, 1997.Google Scholar
  14. 14).
    Hsu, F.-C. and Chen, J.-S., “A Study on Multi Criteria Decision Making Model: Interactive Genetic Algorithms Approach,” inProceedings of IEEE Int. Conf. on System, Man, and Cybernetics (SMC99), pp. 634–639, 1999.Google Scholar
  15. 15).
    Lee, J.-Y. and Cho, S.-B. “Sparse Fitness Evaluation for Reducing User Burden in Interactive Genetic Algorithm,” inProceedings of FUZZ-IEEE 99, II, pp. 998–1003, 1999.Google Scholar
  16. 16).
    Graf, J. and Banzhaf, W., “Interactive Evolutionary Algorithms in Design,”Procs of Artificial Neural Nets and Genetic Algorithms, Ales, France, pp. 227–230, 1995.Google Scholar
  17. 17).
    Ingu, T. and Takagi, H., “Accelerating a GA Convergence by Fitting a Single-peak Function,”IEEE International Conference on Fuzzy Systems FUZZ-IEEE’99, pp. 1415–1420, 1999.Google Scholar
  18. 18).
    Vico, F.J., Veredas, F.J., Bravo, J.M. and Almaraz, J., “Automatic Design Sinthesis with Artificial Intelligence Techniques,”Artificial Intelligence in Engineering 13, pp. 251–256, 1999.CrossRefGoogle Scholar
  19. 19).
    Saez, Y., Sanjuan, O. and Segovia, J., “Algoritmos Geneticos para la Generacion de Modelos con Micropoblaciones,” inProceedings Algoritmos Evolutivos y Bioinspirados (AEB 02), 2002.Google Scholar
  20. 20).
    Santos, A., Dorado, J., Romero, J., Arcay, B. and Rodliguez, J., “Artistic Evolutionary Computer Systems,”Proc. of the GECCO Workshop, Las Vegas, 2000.Google Scholar
  21. 21).
    Unemi, T., “SBART 2.4: An IEC Tool for Creating 2D Images, Movies and Collage,”Proc. of the Genetic and Evolutionary Computation, Conference Program, Las Vegas, 2000.Google Scholar
  22. 22).
    Rowland, D., “Evolutionary Co-operative Design Methodology: The Genetic Sculpture Park,”Proc. of the GECCO Workshop, Las Vegas, 2000.Google Scholar
  23. 23).
    Takagi, H., “Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation,”Proc. of the IEEE, 89, 9, pp. 1275–1296, 2001.CrossRefGoogle Scholar
  24. 24).
    Sugimoto, F. and Yoneyama M., “Robustness Against Instability of Sensory Judgment in a Human Interface to Draw a Facial Image Using a Psychometrical Space Model,”Proceedings of IEEE International Conference on Multimedia and Expo, pp. 653–638, 2000.Google Scholar
  25. 25).
    Dozier, G., “Evolving Robot Behavior via Interactive Evolutionary Computation: from Real-world to Simulation,”Proceedings of the 2001 ACM symposium on Applied computing, pp. 340–344, 2001.Google Scholar
  26. 26).
    Ohsaki, M. and Takagi, H., “Application of Interactive Evolutionary Computation to Optimal Tuning of Digital Hearing Aids,”International Conference on Soft Computing IIZUKA 98, pp. 849–852, 1998.Google Scholar
  27. 27).
    Machado, P. and Cardoso, A., “All the Truth about NEvAr,”Applied Intelligence, Special issue on Creative Systems, 16, 2, pp. 101–119, 2002.MATHGoogle Scholar
  28. 28).
    Saez, Y., Sanjuan, O., Segovia, J. and Isasi, P., “Genetic Algorithms for the Generation of Models with Micropopulations,”Proc. of the EUROGP’03, Univ. of Essex, UK. Apr., 2003.Google Scholar
  29. 29).
    Larrañaga, P. and Lozano, J.A., “Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation,”Kluwer, Boston, MA, 2001.Google Scholar
  30. 30).
    Larrañaga, P., Lozano, J.A. and Bengoetxea, E.,Estimation of Distribution Algorithms Based on Multivariate Normal and Gaussian Networks, KZZA-IK-1-01, 2001.Google Scholar
  31. 31).
    Kern, S., Muller, S.D., Hansen, N., Buche, D., Ocenasek, J. and Koumoutsakos, P.,Learning Probability Distributions in Continuous Evolutionary Algorithms, A Comparative Review, Kluwer Academic Publishers, 2004.Google Scholar

Copyright information

© Ohmsha, Ltd. and Springer 2005

Authors and Affiliations

  • Yago Saez
    • 1
  • Pedro Isasi
    • 1
  • Javier Segovia
    • 1
  • Julio C. Hernandez
    • 1
  1. 1.Universidad CARLOS III de Madrid y UPMMadridSpain

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