Skip to main content

Evolution Ain’t Engineering: Animals, Robots, and the Messy Struggle for Existence

  • Chapter
  • First Online:
Cyborg Futures

Part of the book series: Social and Cultural Studies of Robots and AI ((SOCUSRA))

Abstract

In the sixth edition (1872) of Origin of Species, Darwin lamented: “Great is the power of steady misinterpretation.” He added a hopeful note that misunderstandings in science don’t long endure. But to this day, those about natural selection, the engine of creative adaptation, persist. One abstract notion that dogs evolutionary thinking is “perfection.” In the Galapagos Islands or the robotics laboratory, we see that evolution in-the-flesh suffices, selecting the best-but-not-perfect solutions from among a finite set of options limited by the constraints and trade-offs of physics and genetics. While we can identify different evolutionary processes in retrospect, the randomness that is essential to evolution preordains that outcomes cannot be completely predicted. But even without perfect prediction, we can know that evolving life forms and machines have limits.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
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

Institutional subscriptions

References

  • Bäck, Thomas. 1996. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. New York/Oxford: Oxford University Press.

    Book  Google Scholar 

  • Darwin, Charles. 1859. On the Origin of Species. A Facsimile of the First Edition, 1964. Cambridge, MA/London: Harvard University Press.

    Google Scholar 

  • Deb, Kalyanmoy. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. Vol. 16. Chichester/New York: John Wiley & Sons.

    Google Scholar 

  • ———. 2012. Optimization for Engineering Design: Algorithms and Examples. New Delhi: PHI Learning Pvt. Ltd.

    Google Scholar 

  • Fisher, Ronald A. 1922. “On the Dominance Ratio.” Proceedings of the Royal Society of Edinburgh 42: 321–341.

    Article  Google Scholar 

  • Gordon, Deborah M. 2010. Ant Encounters: Interaction Networks and Colony Behavior. Princeton: Princeton University Press.

    Book  Google Scholar 

  • Grant, Peter R. 1999. Ecology and Evolution of Darwin’s Finches. Princeton: Princeton University Press.

    Google Scholar 

  • Grant, Peter R., and Rosemary B. Grant. 2002. “Unpredictable Evolution in a 30-Year Study of Darwin’s Finches.” Science 296 (5568): 707–711.

    Article  Google Scholar 

  • Hartl, Daniel L., and Andrew G. Clark. 2007. Principles of Population Genetics. 4th ed. Sunderland: Sinauer Associates.

    Google Scholar 

  • Hawkins, Jeff, and Sandra Blakeslee. 2004. On Intelligence: How a New Understanding of the Brain Will Lead to Truly Intelligent Machines. New York: Henry Holt and Company.

    Google Scholar 

  • Jelisavcic, Milan, Matteo de Carlo, Elte Hupkes, Panagiotis Eustratiadis, Jakub Orlowski, Evert Haasdijk, Joshua E. Auerbach, and A.E. Eiben. 2017. “Real-World Evolution of Robot Morphologies: A Proof of Concept.” Artificial Life 23 (2): 206–235.

    Article  Google Scholar 

  • Livingston, Nicholas, Anton Bernatskiy, Kenneth Livingston, Marc L. Smith, Jodi Schwarz, Joshua C. Bongard, David Wallach, and John H. Long Jr. 2016. “Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots.” Frontiers in Robotics and AI 3: 75. https://doi.org/10.3389/frobt.2016.00075.

    Article  Google Scholar 

  • Long, John H., Jr. 2007. “Biomimetic Robotics: Self-Propelled Physical Models Test Hypotheses About the Mechanics and Evolution of Swimming Vertebrates.” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221 (10): 1193–1200.

    Google Scholar 

  • ———. 2012. Darwin’s Devices: What Evolving Robots Can Teach Us About the History of Life and the Future of Technology. New York: Basic Books.

    Google Scholar 

  • Long, John H., Jr., Thomas J. Koob, Kira Irving, Keon Combie, Virginia Engel, Nick Livingston, et al. 2006. “Biomimetic Evolutionary Analysis: Testing the Adaptive Value of Vertebrate Tail Stiffness in Autonomous Swimming Robots.” Journal of Experimental Biology 209 (23): 4732–4746.

    Article  Google Scholar 

  • Pfeifer, Rolf, and Josh Bongard. 2006. How the Body Shapes the Way We Think: A New View of Intelligence. Cambridge, MA: MIT press.

    Book  Google Scholar 

  • Roberts, Sonia F., Jonathan Hirokawa, Hannah G. Rosenblum, Hassan Sakhtah, Andres A. Gutierrez, Marianne E. Porter, and John H. Long Jr. 2014. “Testing Biological Hypotheses with Embodied Robots: Adaptations, Accidents, and By-Products in the Evolution of Vertebrates.” Frontiers in Robotics and AI 1: 12. https://doi.org/10.3389/frobt.2014.00012.

    Article  Google Scholar 

  • Smith, John Maynard. 1989. Evolutionary Genetics. Oxford: Oxford University Press.

    Google Scholar 

  • Sulloway, Frank J. 1984. “Darwin and the Galapagos.” Biological Journal of the Linnean Society 21 (1–2): 29–59.

    Article  Google Scholar 

  • Thrun, Sebastian, Wolfram Burgard, and Dieter Fox. 2005. Probabilistic Robotics. Cambridge, MA: MIT press.

    Google Scholar 

  • Travisano, M., J.A. Mongold, A.F. Bennett, and R.E. Lenski. 1995. “Experimental Tests of the Roles of Adaptation, Chance, and History in Evolution.” Science 267 (5194): 87–90.

    Article  Google Scholar 

  • Wahl, Lindi M. 2011. “Fixation When N and s Vary: Classic Approaches Give Elegant New Results.” Genetics 188 (4): 783–785.

    Article  Google Scholar 

  • Webb, Barbara. 2001. “Can Robots Make Good Models of Biological Behaviour?” Behavioral and Brain Sciences 24 (6): 1033–1050.

    Article  Google Scholar 

  • Webb, Paul W. 1984. “Form and Function in Fish Swimming.” Scientific American 251 (1): 72–83.

    Article  Google Scholar 

  • Weiner, Jonathan. 1994. The Beak of the Finch: A Story of Evolution in Our Time. New York: Vintage.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John H. Long Jr. .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Long, J.H. (2019). Evolution Ain’t Engineering: Animals, Robots, and the Messy Struggle for Existence. In: Heffernan, T. (eds) Cyborg Futures. Social and Cultural Studies of Robots and AI. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-21836-2_2

Download citation

Publish with us

Policies and ethics