Complexity_ A Guided Tour - Melanie Mitchell [154]
Chapter 9
“evolutionary computation”: For a history of early work on evolutionary computation, see Fogel, D. B., Evolutionary Computation: The Fossil Record. New York: Wiley-IEEE Press, 1998.
“That’s where genetic algorithms came from”: John Holland, quoted in Williams, S. Unnatural selection. Technology Review, March 2, 2005.
“automating parts of aircraft design,” Hammond, W. E. Design Methodologies for Space Transportation Systems, 2001, Reston, VA: American Institute of Aeronautics and Astronautics, Inc., p. 548.
“analyzing satellite images”: See, e.g., Harvey, N. R., Theiler, J., Brumby, S. P., Perkins, S. Szymanski, J. J., Bloch, J. J., Porter, R. B., Galassi, M., and Young, A. C. Comparison of GENIE and conventional supervised classifiers for mulitspectral image feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 40, 2002, pp. 393–404.
“automating assembly line scheduling.” Begley, S. Software au naturel. Newsweek, May 8, 1995.
“computer chip design”: Ibid.
“realistic computer-animated horses”: See Morton, O., Attack of the stuntbots. Wired, 12.01, 2004.
“realistic computer-animated stunt doubles”: “Virtual Stuntmen Debut in Hollywood Epic Troy,” news release, NaturalMotion Ltd. [http://www.naturalmotion.com/files/nm_troy.pdf].
“discovery of new drugs”: See, e.g., Felton, M. J., Survival of the fittest in drug design. Modern Drug Discovery, 3(9), 2000, pp. 49–50.
“detecting fraudulent trades”: Bolton, R. J. and Hand, D. J., Statistical fraud detection: A review. Statistical Science, 17(3), 2002, pp. 235–255.
“analysis of credit card data”: Holtham, C., Fear and opportunity. Information Age, July 11, 2007.
“forecasting financial markets”: See, e.g., Williams, F., Artificial intelligence has a small but loyal following. Pensions and Investments, May 14, 2001.
“portfolio optimization”: Coale, K., Darwin in a box. Wired, June 14, 1997.
“artwork created by an interactive genetic algorithm”: see [http://www.karlsims.com].
“I will take you through a simple extended example”: This example is inspired by a project at the MIT Artificial Intelligence Lab, in which a robot named “Herbert” wandered around the halls and offices collecting empty soda cans and taking them to the recycling bin. See Connell, J. H., Minimalist Mobile Robotics: A Colony-Style Architecture for an Artificial Creature. San Diego: Academic Press, 1990.
“This means that there are 243 different possible situations”: There are five different sites each with three possible types of contents, thus there are 3 × 3 × 3 × 3 × 3 = 243 different possible situations.
“Evolutionary algorithms are a great tool”: Jason Lohn, quoted in Williams, S., Unnatural selection. Technology Review, March 2, 2005.
Part III
“The proper domain of computer science”: Quoted in Lewin, R., Complexity: Life at the Edge of Chaos. New York: Macmillan, 1992, p. 48.
Chapter 10
“a recent article in Science magazine”: Shouse, B., Getting the behavior of social insects to compute. Science, 295(5564), 2002, 2357.
“‘Is the brain a computer?’”: Churchland, P. S., Koch, C., and Sejnowski, T. J., What is computational neuroscience? In E. L. Schwartz (editor), Computational Neuroscience. Cambridge, MA: MIT Press, 1994, pp. 46–55.
“The answer is … 2512”: As will be described later in the chapter, to define a rule, you must specify the update state for the center lightbulb given all possible configurations of its local neighborhood. Since a local neighborhood consists of eight neighbors plus the center bulb itself, and each bulb can be either on or off, the number of possible configurations of a local neighborhood is 29 = 512. For each configuration, one can assign either “on” or “off” as the update state, so the number of possible assignments to all 512 configurations is 2512 ≈ 1.3 × 10154.
“The Game of Life”: Much of what is described here can be found in the following sources: Berlekamp, E., Conway, J. H., and