Improved Performance of a Cooperative Genetic Algorithm When Solutions Were Presented as Cartoon Faces

  • Sean R. GreenEmail author
  • Joshua S. Redford
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 1)


Genetic algorithms are often able to identify solutions within a multidimensional problem space that elude human detection. Humans sometimes have difficulty retrospectively identifying the features or feature combinations that lead to the success of an individual solution. However, humans are adept at identifying configural patterns within faces. We hypothesized that mapping the features of a problem space onto a cartoon face space would help humans visualize patterns and contribute to the operation of a genetic algorithm.

In this study, human participants viewed foragers in a virtual environment. The genetic code of each forager was presented either as a list of features or as a cartoon face. Participants selected either one or both parents for mating at each generation. Foraging improved significantly when forager attributes were presented as a face, compared to trials in which attributes were presented as a list. This improvement was found for both human-directed and cooperative genetic algorithms.


evolutionary human-computer interaction perception decisionmaking 


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  1. 1.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
  2. 2.
    De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 9(1), 67–103 (2000)CrossRefGoogle Scholar
  3. 3.
    Dill, K.A., Ozkan, S.B., Weikl, T.R., Chodera, J.D., Voelz, V.A.: The protein folding problem: when will it be solved? Current Opinion in Structural Biology 17, 342–346 (2007)CrossRefGoogle Scholar
  4. 4.
    Kowaliw, T., Dorin, A., McCormack, J.: Promoting creative design in interactive evolutionary computation. IEEE Transactions on Evolutionary Computation 16(4), 523 (2012)CrossRefGoogle Scholar
  5. 5.
    Battiti, R., Passerini, A.: Brain-computer evolutionary multi-objective optimization (PC-EMO): a genetic algorithm adapting to the decision making. IEEE Transactions of Evolutionary Computation 14(5), 671–687 (2010)CrossRefGoogle Scholar
  6. 6.
    Noda, E., Freitas, A.A., Lopes, H.S.: Discovering interesting prediction rules with a genetic algorithm. In: Proceedings of the 1998 Congress on Evolutionary Computation, vol. 2 (1999)Google Scholar
  7. 7.
    Dehuri, S., Mall, R.: Predictive and comprehensible rule discovery using a multi-objective genetic algorithm. Knowledge Based Systems 19, 413–421 (2006)CrossRefGoogle Scholar
  8. 8.
    Domas, C.: The 1s and 0s behind cyber warfare (2013),
  9. 9.
    Farah, M.J., Wilson, K.D., Drain, N., Tanaka, J.N.: What is “special” about face perception. Psychological Review 105(3), 482–498 (1998)CrossRefGoogle Scholar
  10. 10.
    Qian, Z.Q., Teng, H.F., Xiong, D.L., Sun, Z.G.: Human-computer cooperation genetic algorithm and its application to layout design. In: Proceeding of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, Singapore, pp. 299–302 (2002)Google Scholar
  11. 11.
    Kosorukoff, A.: Human based genetic algorithm. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 5, pp. 3464–3469 (2001)Google Scholar
  12. 12.
    Takagi, H.: Interactive evolutionary computation: system optimization based on human subjective evaluation. In: IEEE International Conference on Intelligent Engineering Systems (INES 1998), pp. 17–19 (1998)Google Scholar
  13. 13.
    Boschetti, F., Takagi, H.: Visualization of EC landscape to accelerate EC conversion and evaluation of its effect. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 2, pp. 880–886 (2001)Google Scholar
  14. 14.
    Hayashida, N., Takagi, H.: Acceleration of EC convergence with landscape visualization and human intervention. Applied Soft Computing 1(4), 242–256 (2002)CrossRefGoogle Scholar
  15. 15.
    Bao, J., Sakamoto, G., Nickerson, J.V.: Evaluating design solutions using crowds. In: Proceedings of the Seventeenth Americas Conference on Information Systems, Detroit, Michigan (2011)Google Scholar
  16. 16.
    Guo, Y.-.N., Cheng, J.: Adaptive evaluation strategy based on surrogate models. In: Maurtua, I. (ed.) Human Computer Interaction, pp. 259–277. InTech (2007)Google Scholar
  17. 17.
    Caldwell, C., Johnston, V.S.: Tracking a criminal suspect through face space with a genetic algorithm. In: Proceedings of the Fourth International Conference on Genetic Algorithm, pp. 416–421 (1991)Google Scholar
  18. 18.
    Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Stanley, K.O.: Picbreeder: evolving pictures collaboratively online. In: Proceedings of the Computer Human Interaction Conference, pp. 1759–1768 (2008)Google Scholar
  19. 19.
    Stump, C.: Project PANOPTES: crowdsourcing the search for exoplanets. In: American Astronomical Society Meeting Abstracts 22,Google Scholar
  20. 20.
    Bach-y-Rita, P., Kercel, S.W.: Sensory substitution and the human-machine interface. Trends in Cognitive Sciences 7(12), 541–546 (2003)CrossRefGoogle Scholar
  21. 21.
    Ostrovsky, Y., Meyers, E., Ganesh, S., Sinha, P.: Visual parsing after recovery from blindness. Psychological Science 20, 1484–1491 (2009)CrossRefGoogle Scholar
  22. 22.
    Barry, S.: Fixing my Gaze, p. 272. Basic Books (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of PsychologyUniversity at Buffalo-SIM ProgrammeSingaporeSingapore

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