Advertisement

OMNIREP: originating meaning by coevolving encodings and representations

  • Moshe SipperEmail author
  • Jason H. Moore
Regular Research Paper

Abstract

A major effort in the practice of evolutionary computation goes into deciding how to represent individuals in the evolving population. This task is actually composed of two subtasks: defining a data structure that is the representation and defining the encoding that enables to interpret the representation. In this paper we employ a coevolutionary algorithm—dubbed OMNIREP—to discover both a representation and an encoding that solve a particular problem of interest. We describe four experiments that provide a proof-of-concept of OMNIREP’s essential merit. We think that the proposed methodology holds potential as a problem solver and also as an exploratory medium when scouting for good representations.

Keywords

Evolutionary algorithms Cooperative coevolution Interpretation 

Notes

Acknowledgements

This work was supported by National Institutes of Health grants AI116794, DK112217, ES013508, HL134015, LM010098, LM011360, LM012601, and TR001263.

References

  1. 1.
    Angeline PJ, Pollack JB (1994) Coevolving high-level representations. In: Langton CG (ed) Artificial life III, vol XVII of SFI studies in the sciences of complexity. Addison-Wesley, Santa Fe, pp 55–71Google Scholar
  2. 2.
    Azad RMA, Ryan C (2006) An examination of simultaneous evolution of grammars and solutions. In: Yu T, Riolo R, Worzel B (eds) Genetic programming theory and practice III. Springer, Boston, pp 141–158CrossRefGoogle Scholar
  3. 3.
    Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic programming—an introduction; on the automatic evolution of computer programs and its applications. Morgan Kaufmann, San FranciscozbMATHGoogle Scholar
  4. 4.
    Bentley P, Kumar S (1999) Three ways to grow designs: a comparison of embryogenies for an evolutionary design problem. In: Proceedings of the 1st annual conference on genetic and evolutionary computation-GECCO’99, vol 1. Morgan Kaufmann Publishers Inc., San Francisco, pp 35–43Google Scholar
  5. 5.
    Caraffini F, Neri F, Picinali L (2014) An analysis on separability for memetic computing automatic design. Inf Sci 265:1–22MathSciNetCrossRefGoogle Scholar
  6. 6.
    Correia J, Ciesielski V, Liapis A (2017) Proceedings of computational intelligence in music, sound, art and design: 6th international conference. Springer, BerlinCrossRefGoogle Scholar
  7. 7.
    Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, BerlinCrossRefzbMATHGoogle Scholar
  8. 8.
    Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129MathSciNetzbMATHGoogle Scholar
  9. 9.
    Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., BostonzbMATHGoogle Scholar
  10. 10.
    Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3:493–530MathSciNetzbMATHGoogle Scholar
  11. 11.
    Gruau F, Whitley D, Pyeatt L (1996) A comparison between cellular encoding and direct encoding for genetic neural networks. In: Proceedings of the 1st annual conference on genetic programming. MIT Press, Cambridge, pp 81–89Google Scholar
  12. 12.
    Hart WE, Kammeyer TE, Belew RK (1995) The role of development in genetic algorithms. In: Whitley LD, Vose MD (eds) Foundations of genetic algorithms, vol 3. Elsevier, Amsterdam, pp 315–332Google Scholar
  13. 13.
    Hornby GS, Pollack JB (2002) Creating high-level components with a generative representation for body-brain evolution. Artif Life 8(3):223–246CrossRefGoogle Scholar
  14. 14.
    Iacca G, Caraffini F, Neri F (2014) Multi-strategy coevolving aging particle optimization. Int J Neural Syst 24(01):1450008CrossRefGoogle Scholar
  15. 15.
    Iacca G, Neri F, Mininno E, Ong Y-S, Lim M-H (2012) Ockham’s razor in memetic computing: three stage optimal memetic exploration. Inf Sci 188:17–43MathSciNetCrossRefGoogle Scholar
  16. 16.
    Koza JR (2003) Genetic programming IV: routine human-competitive machine intelligence. Kluwer Academic Publishers, NorwellzbMATHGoogle Scholar
  17. 17.
    Lee CY, Antonsson EK (2000) Variable length genomes for evolutionary algorithms. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San FranciscoGoogle Scholar
  18. 18.
    Mitchell M (1998) An introduction to genetic algorithms. MIT press, CambridgezbMATHGoogle Scholar
  19. 19.
    Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14CrossRefGoogle Scholar
  20. 20.
    Neri F, Cotta C, Moscato P (2012) Handbook of memetic algorithms, vol 379. Springer, BerlinGoogle Scholar
  21. 21.
    Nicolau M, Ryan C (2002) LINKGAUGE: tackling hard deceptive problems with a new linkage learning genetic algorithm. In: Proceedings of the 4th annual conference on genetic and evolutionary computation. Morgan Kaufmann Publishers Inc., San Francisco, pp 488–494Google Scholar
  22. 22.
    Orlov M, Sipper M (2011) Flight of the FINCH through the Java wilderness. IEEE Trans Evol Comput 15(2):166–182CrossRefGoogle Scholar
  23. 23.
    Pena-Reyes CA, Sipper M (2001) Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling. IEEE Trans Fuzzy Syst 9(5):727–737CrossRefGoogle Scholar
  24. 24.
    Ryan C, Collins JJ, O’Neill M (1998) Grammatical evolution: evolving programs for an arbitrary language. In: Proceedings genetic programming, first European workshop, EuroGP’98. Paris, pp 83–96Google Scholar
  25. 25.
    Sipper M, Fu W, Ahuja K, Moore JH (2018) Investigating the parameter space of evolutionary algorithms. BioData Min 11(2):1–14Google Scholar
  26. 26.
    Stanley KO, D’Ambrosio DB, Gauci J (2009) A hypercube-based encoding for evolving large-scale neural networks. Artif Life 15(2):185–212CrossRefGoogle Scholar
  27. 27.
    Stanley KO, Miikkulainen R (2003) A taxonomy for artificial embryogeny. Artif Life 9(2):93–130CrossRefGoogle Scholar
  28. 28.
    Zaritsky A, Sipper M (2004) The preservation of favored building blocks in the struggle for fitness: the puzzle algorithm. IEEE Trans Evol Comput 8(5):443–455CrossRefGoogle Scholar
  29. 29.
    Zhang G, Rong H, Neri F, Pérez-Jiménez MJ (2014) An optimization spiking neural p system for approximately solving combinatorial optimization problems. Int J Neural Syst 24(05):1440006CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Biomedical Informatics (IBI), Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of Computer ScienceBen-Gurion UniversityBeershebaIsrael

Personalised recommendations