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Outline

Evolutionary computation: an overview

1999

Abstract

Evolutionary computation is an area of computer science that uses ideas from biological evolution to solve computational problems. Many such problems require searching through a huge space of possibilities for solutions, such as among a vast number of possible hardware circuit layouts for a configuration that produces desired behavior, for a set of equations that will predict the ups and downs of a financial market, or for a collection of rules that will control a robot as it navigates its environment.

References (79)

  1. Ackley D, Littman M. 1992. Interactions between learning and evolution. In Artifi- cial Life II, ed. CG Langton, C Taylor, JD Farmer, S Rasmussen, pp. 487-509. Read- ing, MA: Addison-Wesley
  2. Adami C. 1998. Introduction to Artificial Life. New York: Springer-Verlag
  3. Angeline PJ, ed. 1997. Evolutionary Pro- gramming VI: 6th Int. Conf. EP97. New York: Springer
  4. Arita T, Koyama Y. 1998. Evolution of lin- guistic diversity in a simple communication system. In Artificial Life VI, ed. C Adami, RK Belew, H Kitano, CE Taylor, pp. 9-17. Cambridge, MA: MIT Press
  5. Axelrod R. 1984. The Evolution of Cooper- ation. New York: Basic
  6. Bäck T. 1996. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algo- rithms. Oxford: Oxford Univ. Press
  7. Bäck T, ed. 1997. Proceedings of the Seventh International Conference on Genetic Algo- rithms, San Francisco, CA: M. Kaufmann
  8. Baeck T, Hammel U, Schwefel HP. 1997. Evolutionary computation: comments on ? the history and current state. IEEE Trans. Evol. Computation 1:3-17
  9. Bak P. 1996. How Nature Works: The Science of Self-Organized Criticality. New York: Springer-Verlag
  10. Belew RK, Mitchell M, eds. 1996. Adap- tive Individuals in Evolving Populations: Models and Algorithms. Reading, MA: Ad- dison Wesley
  11. Belew RK, Vose MD, eds. 1997. Founda- tions of Genetic Algorithms 4. San Fran- cisco, CA: M. Kaufmann
  12. Chomsky N. 1995. The Minimalist Pro- gram. Cambridge, MA: MIT Press
  13. Christiansen FB, Feldman MW. 1998. Al- gorithms, genetics, and populations: the schemata theorem revisited. Complexity 3(3):57-64
  14. Chu J. 1999. Computational explorations of life. PhD thesis. Calif. Inst. Technol., Pasadena, CA
  15. Cliff D, Grand S. 1999. The 'Creatures' global digital ecosystem. Artificial Life. In press
  16. Coyne JA, Barton N, Turelli M. 1997. Per- spective: a critique of Sewall Wright's shifting balance theory of evolution. Evo- lution 51:643-71
  17. Dawkins R. 1989. The evolution of evolv- ability. In Artificial Life, ed. CG Langton, 201-220. Reading, MA: Addison-Wesley
  18. Dawkins R. 1996. The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without Design. New York: Nor- ton. 2nd ed.
  19. Depew DJ, Weber BH. 1995. Darwinism Evolving. Cambridge, MA: MIT Press
  20. Epstein J, Axtell R. 1996. Growing Artifi- cial Societies. Cambridge, MA: MIT Press
  21. Fogel DB. 1995. Evolutionary Compu- tation: Toward a New Philosophy of Machine Intelligence. New York: IEEE Press
  22. Fogel DB, ed. 1998. Evolutionary Com- putation: The Fossil Record. New York: IEEE Press
  23. Fogel LJ, Owens AJ, Walsh MJ. 1966. Arti- ficial Intelligence Through Simulated Evo- lution. New York: John Wiley
  24. Gehlhaar D, Verkhivker G, Rejto P, Sher- man C, Fogel D, et al. 1995. Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexi- ble docking by evolutionary programming. Chem. Biol. 2:317-24
  25. Gold EM. 1967. Language identification in the limit. Inform. Control 10:447-74
  26. Goldberg DE. 1989. Genetic Algorithms in Search, Optimization, and Machine Learn- ing. Reading, MA: Addison-Wesley
  27. Gordon VS, Whitley D. 1993. Serial and parallel genetic algorithms as function op- timizers. In Proc. Fifth Int. Conf. Genetic Algorithms, ed. T Bäck, pp. 177-183. San Mateo, CA: M. Kaufmann
  28. Grefenstette JJ. 1991. Lamarckian learning in multi-agent environments. In Proc. 4th Int. Conf. on Genetic Algorithms and Their Applications, ed. RK Belew, L Booker, pp. 303-10. San Mateo, CA: M. Kaufmann
  29. Hillis WD. 1990. Co-evolving parasites improve simulated evolution as an opti- mization procedure. Physica D 42:228- 34
  30. Hinton GE, Nowlan SJ. 1987. How learn- ing can guide evolution. Complex Systems 1:495-502
  31. Holland JH. 1975. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. Michi. Press
  32. Hopfield J. 1982. Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. USA 79:2554-58
  33. Huynen MA, Stadler F, Fontana W. 1996. Smoothness within a rugged landscape: The role of neutrality in evolution. Proc. Natl. Acad. Sci. USA 93:397-401
  34. Imada A, Araki K. 1996. Lamarckian evo- lution and associative memory. In Proc. 1996 IEEE Third Int. Conf. Evol. Compu- tation (ICES-96):676-80
  35. Juillé H, Pollack JB. 1998. Coevolutionary learning: a case study. In ICML '98-Proc.
  36. Int. Conf. Machine Learning. San Fran- cisco, CA: M. Kaufmann
  37. Kaneko K. 1994. Chaos as a source of com- plexity and diversity in evolution. Artificial Life 1:163-77
  38. Karakotsios K, Bremer M. 1993. SimLife: The Official Strategy Guide. Rocklin, CA: Prima
  39. Kelly K. 1994. Out of Control: The Rise of Neo. Biological Civilization. Reading, MA: Addison-Wesley
  40. Knuth DE. 1973. The Art of Computer Pro- gramming. Vol. 3: Sorting and Searching. Reading, MA: Addison-Wesley
  41. Koza JR. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press
  42. Koza JR, Rice JP, Roughgarden J. 1992. Evolution food foraging strategies for the Caribbean anolis lizard using genetic programming. Adaptive Behav. 1(2):47-74
  43. Lamarck JB. 1809. Philosophie Zoolo- gique, ou Exposition des Considérations Relatives a l'Histoire Naturelle de Ani- maux. Paris: Chez Dentu et L'Auteur
  44. Lamarck JB. 1996. Of the influence of the environment on the activities and habits of animals, and the influence of the activities and habits of these living bodies in modi- fying their organization and structure. See Ref. 10, pp. 39-57
  45. Lewontin R. 1998. Survival of the nicest. NY Rev. Books. 22 Oct. 1998, 59-63
  46. Lindgren K. 1992. Evolutionary phenom- ena in simple dynamics. In Artificial Life II, ed. CG Langton, C Taylor, JD Farmer, S Rasmussen, pp. 295-312. Reading, MA: Addison-Wesley
  47. Maynard Smith J. 1987. Natural selection: when learning guides evolution. Nature 329:761-62
  48. Maynard Smith J. 1992. Byte-sized evolu- tion. Nature 355:772-73
  49. Miglino O, Nafasi K, Taylor CE. 1994. Se- lection for wandering behavior in a small robot. Artificial Life 2:101-16
  50. Mitchell M. 1996. An Introduction to Ge- netic Algorithms. Cambridge, MA: MIT Press
  51. Murakawa M, Yoshizawa S, Adachi T, Suzuki S, Takasuka K, et al. 1998. Ana- logue EHW chip for intermediate fre- quency filters. In Evolvable Systems: From Biology to Hardware, ed. M Sipper, D Mange, pp. 134-43. New York: Springer
  52. Niyogi P, Berwick RC. 1995. The Logical Problem of Language Change. Tech. Rep.
  53. A. I. Memo No. 1516. MIT Artificial Intel- ligence Lab. Cambridge, MA
  54. Papadimtriou CH, Sideri M. 1998. On the evolution of easy instances. Unpublished manuscript, Computer Science Dept., Uni- versity of California, Berkeley, CA
  55. Paredis J. 1997. Coevolving cellular au- tomata: Be aware of the red queen! In Proc. Seventh Int. Conf. Genetic Algo- rithms, ed. T Bäck, pp. 393-400. San Fran- cisco, CA: Morgan Kaufmann
  56. Petzinger Jr. T. 1995. At Deere they know a mad scientist may be the firm's biggest asset. Wall Street J. 14 July 1995, p. A1
  57. Press WH, A. Teukolsky S, Vetterling WT, Flannery BP. 1992. Numerical Recipes in C. New York: Cambridge Univ. Press
  58. Provine WB. 1986. Sewall Wright and Evo- lutionary Biology. Chicago, IL: Univ. Chi- cago Press
  59. Rao SS. 1998. Evolution at warp speed. Forbes Mag.
  60. Rawlins G, ed. 1991. Foundations of Ge- netic Algorithms. San Mateo, CA: M. Kaufmann
  61. Ray TS. 1991. An approach to the synthesis of life. In Artifical Life II, ed. CG Langton, C Taylor, J Farmer, S Rasmussen, pp. 371- 408. Reading, MA: Addison-Wesley
  62. Ray TS, Hart J. 1998. Evolution of differ- entiated multi-threaded digital organisms. In Artificial Life VI, ed. C Adami, RK Belew, H Kitano, CE Taylor, pp. 295-306. Cambridge, MA: MIT Press
  63. Rechenberg I. 1973. Evolutionsstrategie. Stuttgart: Frommann-Holzboog ?
  64. Rosin CD, Belew RK. 1995. Methods for competitive coevolution: finding oppo- nents worth beating. In Proc. Sixth Int. Conf. Genetic Algorithms, ed. LJ Eshel- man, pp. San Francisco, CA: M. Kauf- mann
  65. Sasaki T, Tokoro M. 1997. Adaptation to- ward changing environments: Why Dar- winian in nature? In Proc. Fourth Eur. Conf. on Artificial Life, Cam- bridge, MA: MIT Press
  66. Sasaki T, Tokoro M. 1999. Evolvable learn- able neural networks under changing en- vironments with various rates of inheri- tance of acquired characters: comparison between Darwinian and Lamarckian evo- lution. Artificial Life. In press
  67. Schwefel HP. 1975. Evolutionsstrategie und Numerische Optimierung. PhD thesis, Technische Univ. Berlin, Berlin
  68. Schwefel HP. 1995. Evolution and Opti- mum Seeking. New York: Wiley
  69. Sepkowski JJ. 1992. A Compendium of Fossil Marine Animal Families. Milwau- kee, WI: Milwaukee Public Mus. 2nd ed.
  70. Simon H. 1969. The Sciences of the Artifi- cial. Cambridge, MA: MIT Press
  71. Sims K. 1994. Evolving 3D morphology and behavior by competition. In Artificial Life IV, ed. RA Brooks, P Maes, pp. 28-39. Cambridge, MA: MIT Press
  72. Steels L, Kaplan F. 1998. Stochasticity as a source of innovation in language games. In Artificial Life VI, ed. C Adami, RK Belew, H Kitano, CE Taylor, pp. 368-78. Cam- bridge, MA: MIT Press
  73. Tanese R. 1989. Distributed genetic algo- rithms. In Proc. Third Int. Conf. on Genetic Algorithms, ed. JD Schaffer, pp. 434-39.
  74. van Nimwegen E, Crutchfield JP, Mitchell M. 1999. Statistical dynamics of the Royal Road genetic algorithm. Theoret. Com- puter Sci. To appear 73.
  75. van Nimwegen E, Crutchfield JP, Mitchell M. 1997. Finite populations induce metastability in evolutionary search. Phys. Lett. A, 229(2):144-50
  76. Waddington CH. 1953. Genetic assimila- tion of an acquired character. Evolution 7:118-26
  77. Werner GM, Dyer MG. 1991. Evolution of communication in artificial organisms. In Artificial Life II, ed. CG Langton, C Tay- lor, J Farmer, S Rasmussen, pp. 659-87. Reading, MA: Addison Wesley
  78. Whitley LD, ed. 1993. Foundations of Ge- netic Algorithms 2. San Mateo, CA: M. Kaufmann
  79. Whitley LD, Vose MD, eds. 1995. Foun- dations of Genetic Algorithms 3. San Fran- cisco, CA: M. Kaufmann