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Survival of the flexible: explaining the recent popularity of nature-inspired optimization within a rapidly evolving world

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Abstract

Researchers often comment on the popularity and potential of nature-inspired meta-heuristics (NIM), however there has been a paucity of data to directly support the claim that NIM are growing in prominence compared to other optimization techniques. In a companion article published in this special issue, I reported evidence that the use of NIM is not only growing, but indeed has surpassed mathematical optimization techniques (MOT) and other metaheuristics in several metrics related to academic research activity (publication frequency) and commercial activity (patenting frequency). Motivated by these findings, this article reviews several theories of algorithm utility and discusses why these arguments remain unsatisfying. I argue that any explanation of NIM popularity should directly account for the manner in which most NIM success has actually been achieved: through hybridization and customization to specific problems. By taking a problem lifecycle perspective, this paper provides simple yet important insights into how nature-inspired meta-heuristics might derive utility by being flexible. Given global trends in the evolution of business products and services where optimization algorithms are applied, I speculate that highly flexible algorithm frameworks will become increasingly popular within our rapidly changing world.

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References

  1. Michalewicz Z, Fogel DB (2004) How to solve it: modern heuristics. Springer-Verlag, New York

    MATH  Google Scholar 

  2. Whitacre JM (2011) Recent trends indicate rapidly growing dominance of nature-inspired optimization in academia and industry. Computing (in press). doi:10.1007/s00607-011-0154-z

  3. Holland J (1992) Adaptation in natural and artificial systems. MIT press, Cambridge

    Google Scholar 

  4. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co. Inc., Boston

    MATH  Google Scholar 

  5. Pham QT (2005) Effect of numerical errors on the performance of optimization methods. In: Proceedings of Chemeca, Brisbane, Australia

  6. Fogel DB (2007) Introduction to evolutionary computation. In: Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems

  7. Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9: 303–317

    Article  Google Scholar 

  8. He J, Yao X (2002) From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms. IEEE Trans Evol Comput 6: 495–511

    Article  Google Scholar 

  9. Lehre PK, Yao X (2008) Crossover can be constructive when computing unique input output sequences. Simulated Evol Learn 5361: 595–604

    Article  Google Scholar 

  10. He J et al (2007) A note on problem difficulty measures in black-box optimization: Classification, realizations and predictability. Evol Comput 15: 435–443

    Article  Google Scholar 

  11. Blickle T (1996) Theory of Evolutionary Algorithms and Application to System Synthesis. Swiss Federal Institute of Technology, Switzerland

  12. Wieczorek W, Czech ZJ (2002) Selection Schemes in Evolutionary Algorithms. In: Proceedings of the Symposium on Intelligent Information Systems (IIS’2002), pp 185–194

  13. Van Nimwegen E, Crutchfield JP (2001) Optimizing epochal evolutionary search: population-size dependent theory. Mach Learn 45: 77–114

    Article  MATH  Google Scholar 

  14. Smith T et al (2003) Local evolvability of statistically neutral GasNet robot controllers. Biosystems 69: 223–243

    Article  Google Scholar 

  15. Nijssen S, Back T (2003) An analysis of the behavior of simplified evolutionary algorithms on trap functions. IEEE Trans Evol Comput 7: 11–22

    Article  Google Scholar 

  16. Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins JE (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 69–93

  17. Whitacre JM et al (2009) Making and breaking power laws in evolutionary algorithm population dynamics. Memet Comput 1: 125

    Article  Google Scholar 

  18. Herrera F et al (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12: 265–319

    Article  MATH  Google Scholar 

  19. Eiben A, Smith J (2003) Introduction to evolutionary computing. Springer Verlag, Berlin

    MATH  Google Scholar 

  20. De Jong K (2006) Evolutionary computation: a unified approach. The MIT Press, Cambridge

    MATH  Google Scholar 

  21. Davis L (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  22. Goldberg DE, Voessner S (1999) Optimizing global-local search hybrids. Urbana 51: 61801

    Google Scholar 

  23. Merz P, Freisleben B (1999) A Comparison of Memetic Algorithms, Tabu Search, and Ant Colonies for the Quadratic Assignment Problem. In: congress on evolutionary computation, pp 2063–2070

  24. De Jong KA et al (1995) Using Markov chains to analyze GAFOs. Found genet algorithms 3: 115–137

    Google Scholar 

  25. Back T et al (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1: 3–17

    Article  Google Scholar 

  26. Michalewicz Z (1993) A hierarchy of evolution programs: an experimental study. Evol Comput 1: 51–76

    Article  Google Scholar 

  27. Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, Berlin

    MATH  Google Scholar 

  28. Bonissone PP et al (2006) Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Trans Evol Comput 10: 256

    Article  Google Scholar 

  29. Michalewicz Z (1996) Genetic algorithms + data structures. Springer, Berlin

    MATH  Google Scholar 

  30. De Jong K (1999) Evolving in a changing world. In: Lecture notes in computer science, pp 512–519

  31. Branke J, Mattfeld DC (2005) Anticipation and flexibility in dynamic scheduling. Int J Prod Res 43: 3103–3129

    Article  Google Scholar 

  32. Simon HA (1953) A Behavioral Model of Rational Choice. Santa Monica, Rand Corp

    Google Scholar 

  33. Weick KE et al (2005) Organizing and the process of sensemaking. Organ Sci 16: 409

    Article  Google Scholar 

  34. Färe R, et al. (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review, pp 66–83

  35. Kurzweil R (2001) The law of accelerating returns. KuzweilAI. net. Retrieved Nov 24: 2008

    Google Scholar 

  36. Koh H, Magee CL (2008) A functional approach for studying technological progress: extension to energy technology. Technol Forecast Soc Chang 75: 735–758

    Article  Google Scholar 

  37. Waldner JB (2008) Nanocomputers and swarm intelligence. Wiley-ISTE, London

    Book  Google Scholar 

  38. Walter C (2005) Kryder’s law. Sci Am 293: 32

    Article  Google Scholar 

  39. Achilladelis B et al (1990) The dynamics of technological innovation: The case of the chemical industry* 1. Res Policy 19: 1–34

    Article  Google Scholar 

  40. Argote L, Epple D (1990) Learning curves in manufacturing. Science 247: 920–924

    Article  Google Scholar 

  41. Alberth S (2008) Forecasting technology costs via the experience curve—Myth or magic?. Technol Forecast Soc Chang 75: 952–983

    Article  Google Scholar 

  42. Harmon C (2000) Experience curves of photovoltaic technology. Laxenburg, IIASA

  43. Sood A, Tellis GJ (2005) Technological evolution and radical innovation. J Mark 69: 152–168

    Article  Google Scholar 

  44. Gersick CJG (1991) Revolutionary change theories: a multilevel exploration of the punctuated equilibrium paradigm. Acad Manag Rev 16: 10–36

    Google Scholar 

  45. Griffin A (1993) Metrics for measuring product development cycle time. J Product Innov Manag 10: 112–125

    Article  Google Scholar 

  46. Rosenau MD Jr (1988) Speeding your new product to market. J Consum Mark 5: 23–36

    Google Scholar 

  47. Qualls W et al (1981) Shortening of the PLC: an empirical test. J Mark 45: 76–80

    Article  Google Scholar 

  48. Bettis RA, Hitt MA (1995) The new competitive landscape. Strateg Manag J 16: 7–19

    Article  Google Scholar 

  49. Crawford M (1992) The hidden costs of accelerated product development. J Product Innov Manag 9: 188–199

    Article  Google Scholar 

  50. Millson MR et al (1992) A survey of major approaches for accelerating new product development. J Product Innov Manag 9: 53–69

    Google Scholar 

  51. Page AL (1993) Assessing new product development practices and performance: establishing crucial norms. J Product Innov Manag 10: 273–290

    Article  Google Scholar 

  52. Bayus BL (1994) Are product life cycles really getting shorter?. J Product Innov Manag 11: 300–308

    Article  Google Scholar 

  53. Stalk G (1988) Time–the next source of competitive advantage. Harv Bus Rev 66: 41–51

    Google Scholar 

  54. Stalk G, Hout TM (1990) Competing against time. Free press, New York

    Google Scholar 

  55. Kessler EH, Chakrabarti AK (1996) Innovation speed: a conceptual model of context, antecedents, and outcomes. Acad Manag Rev 21: 1143–1191

    Google Scholar 

  56. Zahra SA et al (2006) Entrepreneurship and dynamic capabilities: a review, model and research agenda. J Manag Stud Oxf 43: 917

    Article  Google Scholar 

  57. Helfat CE, Peteraf MA (2003) The dynamic resource-based view: Capability lifecycles. Strateg Manag J 24: 997–1010

    Article  Google Scholar 

  58. Teece DJ (2007) Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strateg Manag J 28:1319–1350. doi:10.1002/smj.640

    Google Scholar 

  59. Eisenhardt KM, Martin JA (2003) Dynamic capabilities: what are they? In: Helfat CE (ed) The SMS Blackwell handbook of organizational capabilities: emergence, development, and change. Wiley, New York

  60. Eisenhardt K, Tabrizi BN (1995) Accelerating adaptive processes: product innovation in the global computer industry. Adm Sci Q 40: 84–110

    Article  Google Scholar 

  61. Eiben AE, Jelasity (2002) A critical note on experimental research methodology in EC, pp 582–587

  62. Kirschner M, Gerhart J (1998) Evolvability. In: Proceedings of the National Academy of Sciences, vol 95, USA, pp 8420–8427

  63. Gerhart J, Kirschner M (2007) The theory of facilitated variation. Proc Natl Acad Sci 104:8582

    Google Scholar 

  64. Ciliberti S et al (2007) Innovation and robustness in complex regulatory gene networks. Proc Natl Acad Sci 104: 13591–13596

    Article  Google Scholar 

  65. Wagner A (2008) Robustness and evolvability: a paradox resolved. In: Proceedings of the Royal Society of London, Series B: Biological Sciences, vol 275, pp 91–100

  66. Whitacre JM, Bender A (2010) Degeneracy: a design principle for achieving robustness and evolvability. J Theor Biol 263: 143–153

    Article  Google Scholar 

  67. Whitacre JM, Bender A (2010) Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems. Theor Biol Med Model 7:15

    Google Scholar 

  68. Edelman GM, Gally JA (2001) Degeneracy and complexity in biological systems. Proc Natl Acad Sci 98: 13763–13768

    Article  Google Scholar 

  69. Frei R, Whitacre JM, Degeneracy and Networked Buffering: principles for supporting emergent evolvability in agile manufacturing systems. J Nat Comput. Special Issue on Emergent Engineering (in press)

  70. Whitacre JM et al Evolutionary Mechanics: new engineering principles for the emergence of flexibility in a dynamic and uncertain world (http://www.box.net/shared/l56kcd62uk). Nat Comput (in press)

  71. Whitacre JM et al (2010) The role of degenerate robustness in the evolvability of multi-agent systems in dynamic environments. In PPSN XI, Krakow, Poland, pp 284–293

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Whitacre, J.M. Survival of the flexible: explaining the recent popularity of nature-inspired optimization within a rapidly evolving world. Computing 93, 135–146 (2011). https://doi.org/10.1007/s00607-011-0156-x

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