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Human-Based Evolutionary Computing

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Abstract

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.

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References

  • 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 

  • Bentley PJ, Corne DW (2002) Creative evolutionary systems. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Campbell DT (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychol Rev 67:380

    Article  Google Scholar 

  • 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, Springer

    Google Scholar 

  • Dawkins R (1983) Universal Darwinism. In: Bendall DS (ed) Evolution from molecules to men. Cambridge University Press, Cambridge, pp 403–425

    Google Scholar 

  • De Jong KA (1975) Analysis of the behavior of a class of genetic adaptive systems. (Ph.D.), University of Michigan

    Google Scholar 

  • De Jong KA (2006) Evolutionary computation: a unified approach. MIT press, Cambridge

    Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms, 1st edn. Wiley, Chichester/New York

    MATH  Google Scholar 

  • Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, New York

    Book  MATH  Google Scholar 

  • Fleming L, Mingo S, Chen D (2007) Collaborative brokerage, generative creativity, and creative success. Adm Sci Q 52(3):443–475

    Google Scholar 

  • Fogel DB (1994) An introduction to simulated evolutionary optimization. Neural Netw, IEEE Trans 5(1):3–14

    Article  Google Scholar 

  • 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 955–960

    Google Scholar 

  • Gabora L (2005) Creative thought as a non Darwinian evolutionary process. J Creat Behav 39(4):262–283

    Article  Google Scholar 

  • Gendreau M. and Potvin, J-Y (2010), Handbook of Metaheuristics, Springer, New York

    Google Scholar 

  • Gero JS (1996) Creativity, emergence and evolution in design. Knowl Based Syst 9(7):435–448

    Article  Google Scholar 

  • Gero JS, Kazakov VA (1997) Learning and re-using information in space layout planning problems using genetic engineering. Artif Intell Eng 11(3):329–334

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  • 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):12–32

    Article  Google Scholar 

  • 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 Arbor

    Google Scholar 

  • 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):863–882

    Article  Google Scholar 

  • 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 Antonio

    Google Scholar 

  • 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):554–561

    Article  Google Scholar 

  • Kosorukoff AL (2001) Human based genetic algorithm. In: Proceedings of systems, man, and cybernetics, 2001 IEEE international conference on, IEEE, Tucson

    Google Scholar 

  • Kosorukoff AL, Goldberg DE (2001) Genetic algorithms for social innovation and creativity

    Google Scholar 

  • Kyriakou H, Engelhardt S, Nickerson JV (2012) Networks of innovation in 3D printing. Paper presented at the workshop on information in networks

    Google Scholar 

  • Lessig L (2008) Remix: Making art and commerce thrive in the hybrid economy. Penguin Pr, New York

    Book  Google Scholar 

  • Moscato P, Cotta C (2010) A modern introduction to memetic algorithms. In: Handbook of metaheuristics. Springer, New York, pp 141–183

    Google Scholar 

  • Nickerson JV (2013) Crowd work and collective learning. In: Littlejohn A, Margaryan A (eds) Technology-enhanced professional learning. Routledge, New York

    Google Scholar 

  • Nickerson JV, Sakamoto Y (2010) Crowdsourcing creativity: combining ideas in networks. In: Paper presented at the workshop on information in networks

    Google Scholar 

  • Osborn AF (1953) Applied imagination. Scribner, New York

    Google Scholar 

  • Osherson DN, Smith EE (1981) On the adequacy of prototype theory as a theory of concepts. Cognition 9(1):35–58

    Article  Google Scholar 

  • Page SE (2012) Aggregation in agent-based models of economies. Knowl Eng Rev 27(02):151–162

    Article  Google Scholar 

  • Perkins DN (2000) Archimedes’ bathtub: the art and logic of breakthrough thinking. WW Norton, New York

    Google Scholar 

  • Perry-Smith JE, Shalley CE (2003) The social side of creativity: a static and dynamic social network perspective. Acad Manag Rev 28:89–106

    Google Scholar 

  • 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, IEEE

    Google Scholar 

  • Reeves C (2003) Genetic algorithms In: Handbook of metaheuristics. Springer, pp 55–82

    Google Scholar 

  • 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):373–403

    Article  Google Scholar 

  • 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-line

    Google Scholar 

  • Spears WM (1992) Crossover or mutation. Found Genet algorithms 2:221–237

    Google Scholar 

  • Stadler PF, Wagner GP (1997) Algebraic theory of recombination spaces. Evol Comput 5(3):241–275

    Article  Google Scholar 

  • Takagi H (2001) Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc IEEE 89(9):1275–1296

    Article  Google Scholar 

  • 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, Boston

    Google Scholar 

  • 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 Alto

    Google Scholar 

  • Welsh MB (2012) Expertise and the wisdom of crowds: whose judgments to trust and when. In: Proceedings of annual conference, Sapporo, Japan

    Google Scholar 

  • Wisniewski EJ (1997) When concepts combine. Psychon Bull Rev 4(2):167–183

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. Evol Comput, IEEE Trans 1(1):67–82

    Article  Google Scholar 

  • Yu L (2011) Crowd idea generation. (Ph.D.), Stevens Institute of Technology

    Google Scholar 

  • 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, Vancouver

    Google Scholar 

  • Yu L, Nickerson JV (2013) An internet-scale idea generation system. ACM Trans Interact Intell Syst 3(1), Article 2

    Google Scholar 

  • 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 383–392

    Google Scholar 

  • 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):32–49

    Article  Google Scholar 

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Acknowledgements

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

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Authors and Affiliations

  1. Stevens Institute of Technology, Hoboken, USA

    Jeffrey V. Nickerson

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  1. Jeffrey V. Nickerson
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  1. ThinkSplash, LLC, Fairfax, Virginia, USA

    Pietro Michelucci

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Nickerson, J.V. (2013). Human-Based Evolutionary Computing. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_51

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  • DOI: https://doi.org/10.1007/978-1-4614-8806-4_51

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8805-7

  • Online ISBN: 978-1-4614-8806-4

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