Enhanced Innovation: A Fusion of Chance Discovery and Evolutionary Computation to Foster Creative Processes and Decision Making

Xavier Llorà¹, Kei Ohnishi¹, Ying-ping Chen¹, David E. Goldberg¹, and Michael E. Welge²

¹Illinois Genetic Algorithms Laboratory (IlliGAL), National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
xllora@illigal.ge.uiuc.edu
kei@illigal.ge.uiuc.edu
ypchen@illigal.ge.uiuc.edu
deg@illigal.ge.uiuc.edu

²Automated Learning Group, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
welge@ncsa.uiuc.edu

Abstract. Human-based genetic algorithms are powerful tools for organizational modeling. If we enhance them using chance discovery techniques, we obtain an innovative approach for computer-supported collaborative work. Moreover, such a user-centered approach fuses human and computer partners in a natural way. This paper presents a first test, as well as analyzes the obtained results, of real human and computer collaboration powered by the fusion of human-based genetics algorithms and chance discovery.

LNCS 3103, p. 1314 f.

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