Skip to main content
SpringerLink
Log in
Book cover

Handbook of Neuroevolution Through Erlang pp 81–104Cite as

Introduction to Evolutionary Computation

  • Chapter
  • First Online:

  • 1876 Accesses

Abstract

In this chapter we discuss biological evolution, and the way it has evolved the organisms and structures that we see around us today. We then extract the essentials of this natural stochastic search method, and discuss how one could implement the same, or an even more efficient version, in software. Once the standard evolutionary algorithm methods are introduced (genetic algorithms, genetic programming, evolutionary strategies, and evolutionary programming), we also discuss the slightly lesser known memetic algorithm approaches (hybrid algorithms), and how it compares to the already discussed methods. Finally, we discuss the equivalency between all these methods, and the fact that all of them are just different sides of the same coin.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Cracraft J, Donoghue MJ (2004) Assembling the Tree of Life J. Cracraft and M. J. Donoghue, eds. (Oxford University Press), ISBN 0195172345.

    Google Scholar 

  2. Lewontin RC (1970) The Units of Selection. Annual Review of Ecology and Systematics 1, 1-18.

    Article  Google Scholar 

  3. Kimura M (1991) The Neutral Theory of Molecular Evolution: A Review of Recent Evidence. Japan Journal of Genetics 66, 367-386.

    Article  Google Scholar 

  4. Tjivikua T, Ballester P, Rebek J (1990) Self-Replicating System. Journal of the American Chemical Society 112, 1249-1250.

    Article  Google Scholar 

  5. Graur D, Li WH (2000) Fundamentals of Molecular Evolution D. Graur and W.-H. Li, eds. (Sinauer Associates), ISBN 0878932666.

    Google Scholar 

  6. Dawkins R (1976) The Selfish Gene. (Oxford University Press), ISBN 0192860925.

    Google Scholar 

  7. Luke S, Hohn C, Farris J, Jackson G, Hendler J (1997) Co-evolving Soccer Softbot Team Coordination with Genetic Programming. Proceedings of the First International Workshop on RoboCup at the International Joint Conference on Artificial Intelligence 1395: 398-411.

    Google Scholar 

  8. Koza JR (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. (MIT Press), ISBN 0262111705.

    Google Scholar 

  9. Koza JR (1994) Genetic Programming II: Automatic Discovery of Reusable Programs. (MIT Press), ISBN 0262111896.

    Google Scholar 

  10. Koza JR et al (1998) Genetic Programming. Morgan Kaufmann Publishers. ISBN 1558605487.

    Google Scholar 

  11. Holland JH (1975) Adaptation in Natural and Artificial Systems J. H. Holland, ed. (University of Michigan Press).

    Google Scholar 

  12. Mike M (1998) Melanism: Evolution In Action. (Oxford University Press).

    Google Scholar 

  13. Cramer NL (1985) A Representation for the Adaptive Generation of Simple Sequential Programs. In Proceedings of an International Conference on Genetic Algorithms and the Applications, J. J. Grefenstette, ed. (Lawrence Erlbaum Associates), pp. 183-187.

    Google Scholar 

  14. Koza JR, Bennett FH, Andre D, Keane MA (1999) Genetic Programming III: Darwinian Invention and Problem Solving (Morgan Kaufmann), Springer. ISBN 1558605436.

    Google Scholar 

  15. Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2003) Genetic Programming: Routine Human-Competitive Machine Intelligence. (Kluwer Academic Publishers), Springer. ISBN 1402074468.

    Google Scholar 

  16. Koza JR, Keane MA, Yu J, Bennett FH, Mydlowec W (2000) Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming. Genetic Programming and Evolvable Machines 1, 121-164.

    Article  MATH  Google Scholar 

  17. Hans S. (1974) Numerische Optimerung von Computer-Modellen. (PhD thesis).

    Google Scholar 

  18. Back T, Hoffmeister F, Schwefel HP (1991) A Survey of Evolution Strategies. In Proceedings of the Fourth International Conference on Genetic Algorithms, L. B. Belew and R. K. Booker, eds. (Morgan Kaufmann), pp. 2-9.

    Google Scholar 

  19. Auger A, Hansen N (2011) Theory of Evolution Strategies: a New Perspective. In Theory of Randomized Search Heuristics Foundations and Recent Developments, A. Auger and B. Doerr, eds. (World Scientific Publishing), pp. 289-325.

    Google Scholar 

  20. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial Intelligence through Simulated Evolution L. J. Fogel, A. J. Owens, and M. J. Walsh, eds. (John Wiley & Sons).

    Google Scholar 

  21. Moscato P (1989) On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards memetic Algorithms. citeseerx.ist.psu.edu/viewdoc/summary?doi = 10.1.1.27.9474 Accessed March 20 2012

    Google Scholar 

  22. Krasnogor, N. (1999). Coevolution of Genes and Memes in Memetic Algorithms. Proceedings of the 1999 Genetic And Evolutionary Computation Conference Workshop Program, 1999-1999.

    Google Scholar 

  23. Sher GI (2010) DXNN Platform: The Shedding of Biological Inefficiencies. Neuron, 1-36. Available at: http://arxiv.org/abs/1011.6022.

  24. Kassahun Y, Sommer G (2005) Efficient Reinforcement Learning Through Evolutionary Acquisition of Neural Topologies. In Proceedings of the 13th European Symposium on Artificial Neural Networks ESANN 2005 (ACM Press), pp. 259-266.

    Google Scholar 

  25. Siebel NT, Sommer G (2007) Evolutionary Reinforcement Learning of Artificial Neural Networks. International Journal of Hybrid Intelligent Systems 4, 171-183.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL, USA

    Gene I. Sher

Authors
  1. Gene I. Sher
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Sher, G.I. (2013). Introduction to Evolutionary Computation. In: Handbook of Neuroevolution Through Erlang. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4463-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-4463-3_3

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-4462-6

  • Online ISBN: 978-1-4614-4463-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation