Skip to main content
SpringerLink
Log in

Reference chromosome to overcome user fatigue in IEC

  • Special Issue
  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Goldberg, D.E., “Genetic Algorithms in Search, Optimization, and Machine Learning,”Ed. Addison-Wesley, Reading, MA., 1989.

    MATH  Google Scholar 

  2. Grefenstette, J.J., “Optimization of Control Parameters for Generic Algorithms,”IEEE Transactions on Systems, Man, and Cybernetics, 16, 1, pp. 122–128, 1986.

    Article  Google Scholar 

  3. Goldberg, D.E. and Rundnick, M., “Genetic Algorithms and the Variance of Fitness,”Complex Systems, 5, 3, pp. 265–278, 1991.

    MATH  Google Scholar 

  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. 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.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  8. Bentley, P., “From Coffee Tables to Hospitals: Generic Evolutionary Design,”Evolutionary design by computers, Morgan-Kauffman, pp. 405–423, 1999.

  9. Sims, K., “Artificial Evolution for Computer Graphics,”Comp. Graphics, 25, 4, pp. 319–328, 1991.

    Article  MathSciNet  Google Scholar 

  10. Moore, J.H., “GAMusic: Genetic Algorithm to Evolve Musical Melodies,”Windows 3.1 Software available in: http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/genetic/ga/systems/gamusic/0.html., 1994.

  11. Baluja, S., Pomerleau, D. and Jochem, T., “Towards Automated Artificial Evolution for Computer-generated Images,”Connection Science, pp. 325–354, 1994.

  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.

  13. Gonzalez, C., Lozano, J. and Larranarraga, P., “Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems,”Complex Systems, 1997.

  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.

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

  20. Santos, A., Dorado, J., Romero, J., Arcay, B. and Rodliguez, J., “Artistic Evolutionary Computer Systems,”Proc. of the GECCO Workshop, Las Vegas, 2000.

  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.

  22. Rowland, D., “Evolutionary Co-operative Design Methodology: The Genetic Sculpture Park,”Proc. of the GECCO Workshop, Las Vegas, 2000.

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  27. Machado, P. and Cardoso, A., “All the Truth about NEvAr,”Applied Intelligence, Special issue on Creative Systems, 16, 2, pp. 101–119, 2002.

    MATH  Google Scholar 

  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.

  29. Larrañaga, P. and Lozano, J.A., “Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation,”Kluwer, Boston, MA, 2001.

    Google Scholar 

  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.

  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.

Download references

Author information

Authors and Affiliations

  1. Universidad CARLOS III de Madrid y UPM, Campus de Leganés Avda. de la Universidad 30, 28911, Madrid, Spain

    Yago Saez, Pedro Isasi, Javier Segovia & Julio C. Hernandez

Authors
  1. Yago Saez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Isasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Segovia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julio C. Hernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yago Saez.

Additional information

Yago Saez: He received the Computer Engineering degree from the Universidad Pontificia de Salamanca in 1999 Spain. He now is a Ph.D. student and works as assistant professor at the EVANNAI Group at the Computer Science Department of CARLOS III, Madrid, Spain. His main research areas encompasses the interactive evolutionary computation, the design applications and the optimization problems.

Pedro Isasi, Ph.D.: He received Computer Science degree and Ph.D. degree from the Universidad Politécnica de Madrid (UPM), Spain in 1994. He is now working as professor at the EVANNAI Group at the Computer Science Department of CARLOS III, Madrid, Spain. His main research areas are Machine Learning, Evolutionary, Computation and Neural Networks and Applications to Optimization Problems.

Javier Segovia, Ph.D.: He is a receiving physicist, Ph.D. degree in Computer Science (with honours) from the Universidad Politécnica de Madrid (UPM). Currently Dean of the UPM School of Computer Science, and is editor and/or author of more than 70 scientific publications in the fields of genetic algorithms, data and web mining, artificial intelligence and intelligent interfaces.

Julio C. Hernandez, Ph.D.: He has received degree in Maths, Ph.D. degree in Computer Science. His main research area is the artificial intelligence applied to criptography and net security. His unofficial hobbies are chess and go. Currently, he is working as invited researcher at INRIA, France.

About this article

Cite this article

Saez, Y., Isasi, P., Segovia, J. et al. Reference chromosome to overcome user fatigue in IEC. New Gener Comput 23, 129–142 (2005). https://doi.org/10.1007/BF03037490

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03037490

Keywords

Search

Navigation