Human-Based Evolutionary Computing

  • Jeffrey V. Nickerson


Crowds can generate creative ideas by working in parallel to modify and combine each other’s ideas. Specifically, crowd members can be organized by a human-based evolutionary algorithm. New ideas are created from scratch, they are ranked, then selected for modification or combination by other crowd members. The end result of this process is a population of ideas that will be better than starting ideas along all measured dimensions. This technique is particularly useful when problems are difficult to formalize and require human judgment at both the alternative generation and evaluation stages.


Evolutionary Algorithm Pareto Front Memetic Algorithm Floor Plan Evolutionary Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This material is based upon work supported by the National Science Foundation under grants IIS-0855995 and IIS-0968561.


  1. Aerts D, Gabora L, Sozzo S (2013) Concepts and Their Dynamics: A Quantum-Theoretic Modeling of Human Thought, Topics in Cognitive Science 5(5):737–772.Google Scholar
  2. Bentley PJ, Corne DW (2002) Creative evolutionary systems. Morgan Kaufmann, San FranciscoGoogle Scholar
  3. Campbell DT (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychol Rev 67:380CrossRefGoogle Scholar
  4. Cheng CD, Kosorukoff A (2004) Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In: Proceedings of genetic and evolutionary computation–GECCO 2004, Seattle, SpringerGoogle Scholar
  5. Dawkins R (1983) Universal Darwinism. In: Bendall DS (ed) Evolution from molecules to men. Cambridge University Press, Cambridge, pp 403–425Google Scholar
  6. De Jong KA (1975) Analysis of the behavior of a class of genetic adaptive systems. (Ph.D.), University of MichiganGoogle Scholar
  7. De Jong KA (2006) Evolutionary computation: a unified approach. MIT press, CambridgeGoogle Scholar
  8. Deb K (2001) Multi-objective optimization using evolutionary algorithms, 1st edn. Wiley, Chichester/New YorkMATHGoogle Scholar
  9. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, New YorkCrossRefMATHGoogle Scholar
  10. Fleming L, Mingo S, Chen D (2007) Collaborative brokerage, generative creativity, and creative success. Adm Sci Q 52(3):443–475Google Scholar
  11. Fogel DB (1994) An introduction to simulated evolutionary optimization. Neural Netw, IEEE Trans 5(1):3–14CrossRefGoogle Scholar
  12. Füller J, Möslein KM, Hutter K, Haller JBA (2010) Evaluation games–how to make the crowd your Jury. In: Fähnrich KP, Franczyk B (eds) Lecture Notes in Informatics (LNI 175), Proceedings of the Informatik 2010: Service Science – Neue Perspektiven für die Informatik”. Leipzig 2010, pp 955–960Google Scholar
  13. Gabora L (2005) Creative thought as a non Darwinian evolutionary process. J Creat Behav 39(4):262–283CrossRefGoogle Scholar
  14. Gendreau M. and Potvin, J-Y (2010), Handbook of Metaheuristics, Springer, New YorkGoogle Scholar
  15. Gero JS (1996) Creativity, emergence and evolution in design. Knowl Based Syst 9(7):435–448CrossRefGoogle Scholar
  16. Gero JS, Kazakov VA (1997) Learning and re-using information in space layout planning problems using genetic engineering. Artif Intell Eng 11(3):329–334CrossRefGoogle Scholar
  17. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingMATHGoogle Scholar
  18. Hampton JA (1988) Overextension of conjunctive concepts: evidence for a unitary model of concept typicality and class inclusion. J Exp Psychol Learn Mem Cogn 14(1):12–32CrossRefGoogle Scholar
  19. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann ArborGoogle Scholar
  20. Kijkuit B, Van Den Ende J (2007) The organizational life of an idea: integrating social network, creativity and decision-making perspectives. J Manag Stud 44(6):863–882CrossRefGoogle Scholar
  21. Kittur A, Nickerson JV, Bernstein MS, Gerber EM, Shaw AD, Zimmerman J, Lease M, Horton JJ (2013) The future of crowd work. In: Proceedings of 2013 ACM conference on Computer Supported Collaborative Work (CSCW ’13), San AntonioGoogle Scholar
  22. Kohn NW, Paulus PB, Choi YH (2011) Building on the ideas of others: an examination of the idea combination process. J Exp Soc Psychol 47(3):554–561CrossRefGoogle Scholar
  23. Kosorukoff AL (2001) Human based genetic algorithm. In: Proceedings of systems, man, and cybernetics, 2001 IEEE international conference on, IEEE, TucsonGoogle Scholar
  24. Kosorukoff AL, Goldberg DE (2001) Genetic algorithms for social innovation and creativityGoogle Scholar
  25. Kyriakou H, Engelhardt S, Nickerson JV (2012) Networks of innovation in 3D printing. Paper presented at the workshop on information in networksGoogle Scholar
  26. Lessig L (2008) Remix: Making art and commerce thrive in the hybrid economy. Penguin Pr, New YorkCrossRefGoogle Scholar
  27. Moscato P, Cotta C (2010) A modern introduction to memetic algorithms. In: Handbook of metaheuristics. Springer, New York, pp 141–183Google Scholar
  28. Nickerson JV (2013) Crowd work and collective learning. In: Littlejohn A, Margaryan A (eds) Technology-enhanced professional learning. Routledge, New YorkGoogle Scholar
  29. Nickerson JV, Sakamoto Y (2010) Crowdsourcing creativity: combining ideas in networks. In: Paper presented at the workshop on information in networksGoogle Scholar
  30. Osborn AF (1953) Applied imagination. Scribner, New YorkGoogle Scholar
  31. Osherson DN, Smith EE (1981) On the adequacy of prototype theory as a theory of concepts. Cognition 9(1):35–58CrossRefGoogle Scholar
  32. Page SE (2012) Aggregation in agent-based models of economies. Knowl Eng Rev 27(02):151–162CrossRefGoogle Scholar
  33. Perkins DN (2000) Archimedes’ bathtub: the art and logic of breakthrough thinking. WW Norton, New YorkGoogle Scholar
  34. Perry-Smith JE, Shalley CE (2003) The social side of creativity: a static and dynamic social network perspective. Acad Manag Rev 28:89–106Google Scholar
  35. Quiroz JC, Louis SJ, Banerjee A, Dascalu SM (2009) Towards creative design using collaborative interactive genetic algorithms. In: Proceedings of evolutionary computation, 2009. CEC’09. IEEE congress on, IEEEGoogle Scholar
  36. Reeves C (2003) Genetic algorithms In: Handbook of metaheuristics. Springer, pp 55–82Google Scholar
  37. Secretan J, Beato N, D’Ambrosio DB, Rodriguez A, Campbell A, Folsom-Kovarik JT, Stanley KO (2011) Picbreeder: a case study in collaborative evolutionary exploration of design space. Evol Comput 19(3):373–403CrossRefGoogle Scholar
  38. Seneviratne O, Monroy-Hernandez A (2010) Remix culture on the web: a survey of content reuse on different user-generated content websites. Paper presented at the proceedings of WebSci10: extending the frontiers of society on-lineGoogle Scholar
  39. Spears WM (1992) Crossover or mutation. Found Genet algorithms 2:221–237Google Scholar
  40. Stadler PF, Wagner GP (1997) Algebraic theory of recombination spaces. Evol Comput 5(3):241–275CrossRefGoogle Scholar
  41. Takagi H (2001) Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc IEEE 89(9):1275–1296CrossRefGoogle Scholar
  42. Tanaka Y, Sakamoto Y, Kusumi T (2011) Conceptual combination versus critical combination: Devising creative solutions using the sequential application of crowds. In: Proceedings of annual meeting of the cognitive science society, BostonGoogle Scholar
  43. Tuite K, Smith AM, Studio EI (2012) Emergent remix culture in an anonymous collaborative art system. In: Proceedings of eighth artificial intelligence and interactive digital entertainment conference, Palo AltoGoogle Scholar
  44. Welsh MB (2012) Expertise and the wisdom of crowds: whose judgments to trust and when. In: Proceedings of annual conference, Sapporo, JapanGoogle Scholar
  45. Wisniewski EJ (1997) When concepts combine. Psychon Bull Rev 4(2):167–183CrossRefGoogle Scholar
  46. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. Evol Comput, IEEE Trans 1(1):67–82CrossRefGoogle Scholar
  47. Yu L (2011) Crowd idea generation. (Ph.D.), Stevens Institute of TechnologyGoogle Scholar
  48. Yu L, Nickerson JV (2011) Cooks or cobblers? Crowd creativity through combination. In: Proceedings of the 29th CHI conference on human factors in computing systems, ACM Press, VancouverGoogle Scholar
  49. Yu L, Nickerson JV (2013) An internet-scale idea generation system. ACM Trans Interact Intell Syst 3(1), Article 2Google Scholar
  50. Yu L, Sakamoto Y (2011) Feature selection in crowd creativity. In: Schmorrow DD, Fidopiastis CM (eds) Foundations of augmented cognition. Directing the future of adaptive systems. Springer, New York pp 383–392Google Scholar
  51. Zhou A, Qu B-Y, Li H, Zhao S-Z, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32–49CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jeffrey V. Nickerson
    • 1
  1. 1.Stevens Institute of TechnologyHobokenUSA

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