skip to main content
10.1145/1276958.1277160acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Achieving a simple development model for 3D shapes: are chemicals necessary?

Authors Info & Claims
Published:07 July 2007Publication History

ABSTRACT

Artificial Development Systems have been introduced as a technique aimed at increasing the scalability of evolutionary algorithms. Most commonly the development model is part of the evolutionary process, each individual developed during fitness evaluation. To achieve scalability it may be argued that the implicit requirements of evolvability and effectivity ( in terms of its resource requirements) are thus placed on the development model. To achieve an effective development model, one of the challenges is to find appropriate mechanisms from developmental biology and ways to implement them for the application in hand. This work presents a development model for the evolution and development of 3D shapes. The goal being to create a simple development model for any 3D shape. Further, this work provides a preliminary investigation into the usefulness of one of the mechanisms implemented in this model, that of chemicals.

References

  1. P. Bentley and S. Kumar. Three ways to grow designs: A comparison of embryogenies for an evolutionary design problem. In Proceedings of the Genetic and Evolutionary Computation Conference volume1, pages 35--43, Orlando, Florida, USA, 13-17July 1999. Morgan Kaufmann.Google ScholarGoogle Scholar
  2. J. Bongard and R. Pfeifer. Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny. In Proceedings of the Genetic and Evolutionary Computation Conference pages 820--836. Morgan-Kaufmann, 2001.Google ScholarGoogle Scholar
  3. J. Bongard and R. Pfeifer. Evolving complete agents using artificial ontogeny. In F. Hara and R. Pfeifer, editors, Morpho-functional Machines: The New Species (Designing Embodied Intelligence) pages 237--258. Springer-Verlag, 2003.Google ScholarGoogle Scholar
  4. F. Dellaert. Toward A biologically defensible model of development. Master's thesis, Nov. 1995.Google ScholarGoogle Scholar
  5. P. Eggenberger. Evolving morphologies of simulated 3d organisms based on differential expression, 1997.Google ScholarGoogle Scholar
  6. D. Federici. Evolving complete agents using artificial ontogeny. In Proceedings of SAB, Simulation of Adaptive Behavior pages 373--384, 2004.Google ScholarGoogle Scholar
  7. e. a. G. M. Rubin. Comparative genomics of the eukaryotes. In Science pages 2204--2215. 2000.Google ScholarGoogle Scholar
  8. D. Goldberg, K. Deb, and B. Korb. Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3: 493--530, 1989.Google ScholarGoogle Scholar
  9. D. E. Goldberg, K. Deb, H. Kargupta, and G. Harik. Rapid accurate optimization of difficult problems using fast messy genetic algorithms. In S. Forrest, editor, Proc. of the Fifth Int. Conf. on Genetic Algorithms pages 56--64, San Mateo, CA, 1993. Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. G. R. Harik and D. E. Goldberg. Learning linkage. In R. K. Belew and M. D. Vose, editors, Foundations of Genetic Algorithms 4 pages 247--262. Morgan Kaufmann, San Francisco, CA, 1997.Google ScholarGoogle Scholar
  11. P. Hogeweg. Shapes in the shadow: Evolutionary dynamics of morphogenesis. In Artificial Life volume 6, pages 85--101, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P. E. Hotz. Comparing direct and developmental encoding schemes in artificial evolution: A case study in evolving lens shapes. In Proc. of the 2004 IEEE Congress on Evolutionary Computation pages 752--757, Portland, Oregon, 20-23 June 2004.Google ScholarGoogle ScholarCross RefCross Ref
  13. T. Kalganova. Bidirectional incremental evolution in extrinsic evolvable hardware. In J. Lohn, A. Stoica, and D. Keymeulen, editors, Proceedings of the Second NASA/DoD Workshop on Evolvable Hardware (EH2000) pages 65--74, Palo Alto, California, USA, 13--15 2000. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. Kumar and P. J. Bentley. Implicit evolvability: An investigation into the evolvability of an embryogeny. In Late Breaking Papers in The Second Genetic and Evolutionary Computation Conference (GECCO 2000) 2000.Google ScholarGoogle Scholar
  15. J. F. Miller. Evolving a self-repairing, self-regulating, french flag organism. In Proceedings of the Genetic and Evolutionary Computation Conference pages 129--139, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  16. J. F. Miller and W. Banzhaf. Evolving the program for a cell: from french flags to boolean circuits. In S. Kumar and P. J. Bentley, editors, On Growth, Form and Computers Academic Press, Oct. 2003.Google ScholarGoogle Scholar
  17. H. Muhlenbein and D. Schlierkamp-Voosen. Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computation 1(1): 25--49, 1993.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. G. T. P. C. Haddow and P. van Remortel. Shrinking the genotype: L-systems for ehw? In Proceedings of International Conference on evolvable systems: from Biology to Hardware pages 128--139, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. R. C. Purshouse and P. J. Fleming. An Adaptive Divide-and-Conquer Methodology for Evolutionary, Multi-criterion Optimisation. In C. M. Fonseca, P. J. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors, Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003 pages 133--147, Faro, Portugal, April 2003. Springer. Lecture Notes in Computer Science. Volume 2632. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Torresen. A divide and conquer approach to evolvable hardware. In Proc. of the Second Int. Conf. on Evolvable Systems: from Biology to Hardware (ICES98) 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. L. Wolpert. In Principles of Development Oxford University Press, 1998.Google ScholarGoogle Scholar

Index Terms

  1. Achieving a simple development model for 3D shapes: are chemicals necessary?

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
      July 2007
      2313 pages
      ISBN:9781595936974
      DOI:10.1145/1276958

      Copyright © 2007 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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 July 2007

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • Article

      Acceptance Rates

      GECCO '07 Paper Acceptance Rate266of577submissions,46%Overall Acceptance Rate1,669of4,410submissions,38%

      Upcoming Conference

      GECCO '24
      Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader