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Interactive Evolutionary Computation algorithms applied to solve Rastrigin test functions

  • Yago Saez
  • Pedro Isasi
  • Javier Segovia
Part of the Advances in Soft Computing book series (AINSC, volume 29)

Summary

this paper presents a new approach to interactive evolutionary computation that helps the user in the difficult task of finding an optimal solution between multiple possibilities. There are several ways of applying algorithms in interactive evolutionary computation; in this paper we explain three of them in order to make an experimental comparative study. Proceeding with a main goal of solving complex problems as fast as possible, we take the Rastrigin test function as a benchmark and it is executed with the three algorithms described. The aim is to show clearly the results of the algorithms in terms of solution quality and number of iterations. The results clearly show that the use of the proposed method based on chromosome learning heuristics works well even for non Interactive Evolutionary Computation frameworks.

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References

  1. [1]
    Dawkins R. (1986) “The Blind Watchmaker”, Longman Scientific and Technical, Harlow.Google Scholar
  2. [2]
    Bentley P. (1999) “From Coffee Tables to Hospitals: Generic Evolutionary Design”, Evolutionary design by computers, Morgan-Kauffman, pp. 405–423.Google Scholar
  3. [3]
    Ngo J. T. and Marks J. (1993), “Spacetime Constraints Revisited”. Computer Graphics, Annual Conference Series pp. 335–342.Google Scholar
  4. [4]
    Sims K., (1991) Artificial Evolution for Computer Graphics, Comp. Graphics, Vol. 25, No. 4, pp. 319–328.CrossRefMathSciNetGoogle Scholar
  5. [5]
    Sims K., (July 1994) Evolving Virtual Creatures. In Computer Graphics. Annual Conference Series (SIGGRAPH’ 94 Proceedings), pp. 15–22.Google Scholar
  6. [6]
    Sims K., (1994) Evolving 3D Morphology and Behaviour Schemes. In Fogel, L. J. Angeline, P.J. and Back, T. Proc. of the 5th Annual Conference on Evolutionary Programming, Cambridge, MA: MIT Press, pp. 121–129.Google Scholar
  7. [7]
    Moore, J. H. (1994) GAMusic: Genetic algorithm to evolve musical melodies. Software available in ‘http://www.cs.cmu.edu/afs/cs/project/airepository/ai/areas/genetic/ga/systems/gamusic/0.html’ Google Scholar
  8. [8]
    Graf J., Banzhaf W (1995). Interactive Evolutionary Algorithms in Design. Proceedings of Artificial Neural Nets and Genetic Algorithms, Ales, France; pp. 227–230.Google Scholar
  9. [9]
    Vico F.J., Veredas F.J, Bravo J.M., Almaraz J., (1999) Automatic design synthesis with artificial intelligence techniques. Artificial Intelligence in Engineering 13, pp. 251–256.CrossRefGoogle Scholar
  10. [10]
    Unemi T. (2000) SBART 2.4: an IEC Tool for Creating 2D images, movies and collage, Proc. of the Genetic and Evolutionary Computation Conference Program, Las Vegas, pp. 153–157.Google Scholar
  11. [11]
    Rowland D. (2000) Evolutionary Co-operative Design Methodology: The genetic sculpture park. Proc. of the GECCO Workshop, Las Vegas, pp. 75–79.Google Scholar
  12. [12]
    Berlanga A., Isasi P. Segovia J. Interactive Evolutionary (2001) Computation with Small Population to Generate Gestures in Avatars, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 823–828.Google Scholar
  13. [13]
    Hsu F.-C., Chen J.-S. (1999), “A study on multi criteria decision making model: Interactive genetic algorithms approach”, IEEE Int. Conf. on System, Man, and Cybernetics (SMC99), pp. 634–639.Google Scholar
  14. [14]
    Nishio K., Murakami M., Mizutani E., Honda N. (1995) “Efficient fuzzy fitness assignment strategies in an interactive genetic algorithm for cartoon face search”, In Proc. Sixth International Fuzzy Systems Association World Congress (IFSA’95), pp. 173–176.Google Scholar
  15. [15]
    Baluja S., Pomerleau D., Jochem T. (1994) “Towards Automated Artificial Evolution for Computer-generated Images”, Connection Science, Vol. 6, No. 2,3, pp 325–354.CrossRefGoogle Scholar
  16. [16]
    Baluja S., (1998) “Using Knowledge To Create Probabilistic Models For Optimization”. http://citeseer.ist.psu.edu/602315.html, pp. 1–26.Google Scholar
  17. [17]
    Törn A., Zilinskas A. (1989), “Global optimization”, 0-387-50871-6, Springer-Verlag, New York, Inc.zbMATHGoogle Scholar
  18. [18]
    Mühlenbein H., Schomisch D., Born J. (1991) “The Parallel Genetic Algorithm as Function Optimizer”, Parallel Computing, Vol. 17, No. 6,7, pp. 619–632.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yago Saez
    • 1
  • Pedro Isasi
    • 1
  • Javier Segovia
    • 2
  1. 1.Department of Artificial IntelligenceUniversity Carlos III of MadridMadrid
  2. 2.Systems and Languages department, Faculty of Computer ScienceUniversity Politécnica of MadridMadrid

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