Artificial Intelligence Review

, Volume 38, Issue 1, pp 41–54 | Cite as

On the origin of the evolutionary computation species influences of Darwin’s theories on computer science



This paper presents a small sample of evidences of the direct and clear influence of the Darwin’s Theory of Evolution on the Computer Science field, putting the core seed of the well-known Evolutionary Computation and making Computer Science overcome some previous algorithmic limitations. The paper also shows how the more faithful to the Evolution Theory the algorithms, the better their performance and robustness, thus uncovering the crucial importance of the ideas collected in “On the Origin of Species” for the development of Computation and, indirectly through this, for the development of a great diversity of knowledge areas.


Evolutionary computation Genetic algorithms Darwinism Artificial life Bio-inspiration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Altshuler E, Linden D (1977) Design of a wire antenna using a genetic algorithm. J Electron Def 20(7): 50–52Google Scholar
  2. Applebaum P (2000) Darwin. Norton, W. W. & Company, New YorkGoogle Scholar
  3. Arifovic J (2001) Evolutionary dynamics of currency substitution. J Econ Dyn Control 25: 395–417MATHCrossRefGoogle Scholar
  4. Ashley S (1992) Engineous explores the design space. Mechanical Engineering, pp 49–52Google Scholar
  5. Axelrod R (1984) The evolution of cooperation. Basic Books, New YorkGoogle Scholar
  6. Bäck T (1998) On the behavior of evolutionary algorithms in dynamic fitness landscapes. In: Proceedings of IEEE international conference on evolutionary computation, IEEE Press, pp 446–451Google Scholar
  7. Banzhaf W, Eeckman FH (1995) Evolution and biocomputation, Lecture notes on computer science, vol 899. Springer, BerlinGoogle Scholar
  8. Batten D (2008) Genetic algorithms—do they show that evolution works? Available via Accessed 12 Dec 2008
  9. Beasley JE, Sonander J, Havelock P (2001) Scheduling aircraft landings at london heathrow using a population heuristic. J Oper Res Soc 52(5): 483–493MATHCrossRefGoogle Scholar
  10. Benini E, Toffolo A (2002) Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation. J Sol Energy Eng 124(4): 357–363CrossRefGoogle Scholar
  11. Bremermann J (1962) Optimization through evolution and recombination. Spartan Books, Washinton D.C., pp 93–106Google Scholar
  12. Cannon W (1932) The wisdom of the body. Norton and Company, New YorkGoogle Scholar
  13. Cantú-Paz E (2001) Migration policies, selection pressure, and parallel evolutionary algorithms. J Heuristics 7(4): 311–334MATHCrossRefGoogle Scholar
  14. Castillo MDD, Gasós J, García-Alegre M (1993) Genetic processing of the sensorial information. Sens Actuators A 37-38: 255–259CrossRefGoogle Scholar
  15. Charbonneau P (1995) Genetic algorithms in astronomy and astrophysics. Astrophys J Suppl Ser 101: 309–334CrossRefGoogle Scholar
  16. Cobb H, Grefenstette J (1993) Genetic algorithms for tracking changing environments. In: Proceedings of the fifth international conference on genetic algorithms. Morgan Kaufman, San Francisco, pp 523–530Google Scholar
  17. Darwin CR (1979) The origin of species, reprint of the 1976 issue of the 1968 edition published by penguin books edn. Gramercy Books, USAGoogle Scholar
  18. Dawkins R (1996) The blind watchmaker: why the evidence of evolution reveals a universe without design. W.W. Norton, New YorkGoogle Scholar
  19. DeJong KA (2006) Evolutionary computation: a unified approach. MIT Press, Cambridge, MAGoogle Scholar
  20. Dembski W (2002) No free lunch: why specified complexity cannot be purchased without intelligence. Rowman & Littlefield, Lanham, MarylandMATHGoogle Scholar
  21. Dewey J (1965) The influence of Darwin on philosophy: and other essays in contemporary thought. H. Holt and Company, BloomintongGoogle Scholar
  22. Duffy J, Feltovich N (1999) Observation of others affect learning in strategic environments? an experimental study. Int J Game Theory 28: 131–152MathSciNetMATHCrossRefGoogle Scholar
  23. Ellwood CA (1909) The influence of darwin on sociology. Psychol Rev 16: 188–194CrossRefGoogle Scholar
  24. Fogel DB, Chellapilla K, Angeline P (2002) Evolutionary computation and economic models: sensitivity and unintended consequences. Physica-Verlag, New York, pp 245–269Google Scholar
  25. Fogel LJ (1999) Artificial intelligence through simulated evolution: forty years of evolutionary programming. John Wiley & Sons, New YorkGoogle Scholar
  26. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, Chichester, WS, UKMATHGoogle Scholar
  27. Fraser AS (1957) Simulation of genetic systems by automatic digital computers i: introduction. Aust J Biol Sci 10: 484–491Google Scholar
  28. Fraser AS (1957) Simulation of genetic systems by automatic digital computers ii: Effects of linkage on rates of advance under selection. Aust J Biol Sci 10: 492–499Google Scholar
  29. Fraser AS (1957) Simulation of genetic systems by automatic digital computers vi: epistasis. Aust J Biol Sci 13: 150–162Google Scholar
  30. Friedberg RM (1958) A learning machine: Part i. IBM J Res Dev 2(1): 2–13MathSciNetCrossRefGoogle Scholar
  31. Friedberg RM, Dunham B, North JH (1959) A learning machine: part ii. IBM J Res Dev 3(3): 282–287MathSciNetCrossRefGoogle Scholar
  32. Friedman G (1956) Select feedback computers for engineering synthesis and nervous system analogy. Master’s thesis, UCLA, Los AngelesGoogle Scholar
  33. Giro R, Cyrillo M, Galvão DS (2002) Designing conducting polymers using genetic algorithms. Chem Phys Lett 366(1–2): 170–175CrossRefGoogle Scholar
  34. Glen RC, Payne AWR (1995) A genetic algorithm for the automated generation of molecules within constraints. J Comput Aided Mol Des 9: 181–202CrossRefGoogle Scholar
  35. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, ReadingMATHGoogle Scholar
  36. Haas OCL, Bumham KJ, Mills JA (1997) On improving physical selectivity in the treatment of cancer: A systems modelling and optimisation approach. Control Eng Pract 5(12): 1739–1745CrossRefGoogle Scholar
  37. Haupt R, Haupt SE (1998) Practical genetic algorithms. Wiley, New YorkMATHGoogle Scholar
  38. Hayden J (1909) Darwin and evolutionary ethics. Psychol Rev 16: 195–206CrossRefGoogle Scholar
  39. Hoffman A (1989) Arguments on evolution: a paleontologist’s perspective. Oxford University Press, New YorkGoogle Scholar
  40. Holland JH (1962) Outline for a logical theory of adaptive systems. J ACM 9(3): 279–314CrossRefGoogle Scholar
  41. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  42. Hornby G (2003) Generative representations for evolving families of designs. In: Proceedings of Genetic and Evolutionary Computation Conference 2003. Springer, Berlin, pp 1678–1689Google Scholar
  43. Jensen M (2003) Generating robust and flexible job shop schedules using genetic algorithms. IEEE Trans Evol Comput 7(3): 275–288CrossRefGoogle Scholar
  44. Keber C (2002) Evolutionary computation in option pricing: determining implied volatilities based on american put options. Physica-Verlag, New York, pp 399–415Google Scholar
  45. Kewley R, Embrechts M (2002) Computational military tactical planning system. IEEE Trans Syst Man Cybern Part C Appl Rev 32(2): 161–171CrossRefGoogle Scholar
  46. Kicinger R, Arciszewski T, DeJong K (2004) Morphogenesis and structural design: Cellular automata representations of steel structures in tall buildings. In: Proceedings of the congress of evolutionary computation 2004. IEEE Press, pp 41–418Google Scholar
  47. Koza J (1992) A genetic approach to econometric modeling. Pergamon Press, Oxford, UK, pp 57–75Google Scholar
  48. Koza J, Bennett F, Andre D, Keane MA (1999) Genetic programming III: Darwinian invention and problem solving. Morgan Kaufmann Publishers, San FranciscoMATHGoogle Scholar
  49. Langdon WB, Poli R (2002) Foundations of genetic programming. Springer, BerlinMATHGoogle Scholar
  50. Laurent J, Nightingale J (eds) (2001) Darwinism and evolutionary economics. Edward Elgar PublishingGoogle Scholar
  51. Lee Y, Zak SH (2002) Designing a genetic neural fuzzy antilock-brake-system controller. IEEE Trans Evol Comput 6(2): 198–211CrossRefGoogle Scholar
  52. Li J (2006) Enhancing financial decision making using multi-objective financial genetic programming. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2006). Vancouver, Canada, pp 7935–7942Google Scholar
  53. Morrison R (2004) Designing evolutionary algorithms for dynamic environments. Springer, BerlinMATHGoogle Scholar
  54. Morrison R, DeJong K (1999) A test problem generator for non-stationary environments. In: Michalewicz Z, Shoenauer M, Yao Z, Zalzala A (eds) Proceedings of the 1999 congress on evolutionary computation. IEEE Press, New York, pp 7935–7942Google Scholar
  55. Naik G (1996) Back to darwin: In sunlight and cells, science seeks answers to high-tech puzzles. The Wall Street Journal January(16th):A1Google Scholar
  56. Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B Cybern 366(1): 141Google Scholar
  57. Pereira R (2002) Forecasting ability but no profitability: an empirical evaluation of genetic algorithm-optimised technical trading rules. Physica-Verlag, New York, pp 287–309Google Scholar
  58. Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming.
  59. Rechenberg I (1973) Evolutionsstrategie—optimierung technischer systeme nach prinzipien der biologischen evolution. PhD thesis, reprinted by Fromman-HolzboogGoogle Scholar
  60. Reynolds RG (1999) An overview of cultural algorithms: advances in evolutionary computation. McGraw Hill Press, New YorkGoogle Scholar
  61. Rizki M, Zmuda M, Tamburino L (2002) Evolving pattern recognition systems. IEEE Trans Evol Comput 6(6): 594–609CrossRefGoogle Scholar
  62. Rowland J (1909) The influence of darwin on psychology. Psychol Rev 16: 152–169CrossRefGoogle Scholar
  63. Sambridge M, Gallagher K (1993) Earthquake hypocenter location using genetic algorithms. Bull Seismol Soc Am 83(5): 1467–1491Google Scholar
  64. Sarma J (1998) An analysis of decentralized and spatially distributed genetic algorithms. PhD thesis, George Mason University, VirginiaGoogle Scholar
  65. Sasaki D, Morikawa M, Obayashi S, Nakahashi K (2001) Aerodynamic shape optimization of supersonic wings by adaptive range multiobjective genetic algorithms. In: Zitzler E, Deb K, Thiele L, Coello CA, Corne DW (eds) Evolutionary multi-criterion optimization: proceedings of the first international conference EMO 2001. Springer, Zurich, Switzerland, pp 639–652Google Scholar
  66. Sato S, Otori K, Takizawa A, Sakai H, Ando Y, Kawamura H (2002) Applying genetic algorithms to the optimum design of a concert hall. J Sound Vib 258(3): 517–526CrossRefGoogle Scholar
  67. Schechter B (2000) Putting a darwinian spin on the diesel engine. The New York Times September(19th):F3Google Scholar
  68. Serrano JI, del Castillo MD (2007) Evolutionary learning of document categories. Inf Retr 10(1): 69–83CrossRefGoogle Scholar
  69. Serrano JI, Alonso J, del Castillo MD, Naranjo JE (2005) Evolutionary optimization of autonomous vehicle tracks. In: Proceedings of the IEEE congress on evolutionary computation (CEC) 2005. IEEE Computer Society Press, Edinburgh, UK, pp 1332–1339Google Scholar
  70. Seymour-Smith M (1998) 100 most influential books ever written. Citadel Press, SecaucusGoogle Scholar
  71. Skolicki Z, DeJong K (2004) Improving evolutionary algorithms with multi-representation island models. In: Proceedings of parallel problem solving from nature VIII, Springer, pp 420–429Google Scholar
  72. Spears W (1994) Simple subpopulation schemes. In: Sebald A (ed) Proceedings of the third conference on evolutionary programming. World Scientific Publisher, pp 297–307Google Scholar
  73. Stanley K (2004) Efficient evolution of neural networks through complexification. PhD thesis, University of Texas, AustinGoogle Scholar
  74. Todd S, Latham W (1992) Evolutionary art and computers. Academic Press, OrlandoMATHGoogle Scholar
  75. Turing A (1950) Computing machinery and intelligence. Mind 59: 94–101MathSciNetGoogle Scholar
  76. Weismann D, Hammel U, Bäck T (1998) Robust design of multilayer optical coatings by means of evolutionary algorithms. IEEE Trans Evol Comput 2(4): 162–167CrossRefGoogle Scholar
  77. Whitley D, Rana S, Hechendom R (1999) The island model genetic algorithm: on separability, population size and convergence. J Comput Inf Technol 2(1): 33–47Google Scholar
  78. Williams E, Crossley W, Lang T (2001) Average and maximum revisit time trade studies for satellite constellations using a multiobjective genetic algorithm. J Astronaut Sci 49(3): 385–400Google Scholar
  79. Wright S (1932) The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Proceedings of the 6th international congress on genetics, pp 356–366Google Scholar
  80. Yan W, Clark CD (2007) Evolving robust gp solutions for hedge fund stock selection in emerging markets. In: Proceedings of the genetic and evolutionary computation conference GECCO’07. ACM Press, New York, pp 2234–2241Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • J. Ignacio Serrano
    • 1
  • M. Dolores del Castillo
    • 1
  1. 1.Consejo Superior de Investigaciones Científicas (CSIC)Arganda del ReySpain

Personalised recommendations