INNOVATION AND CREATIVITY SUPPORT VIA CHANCE DISCOVERY, GENETIC ALGORITHMS, AND DATA MINING

Creativity protocols and methodologies tend to be time consuming if applied manually. This paper presents how information technologies can support innovation and creativity for collaborative scenario creation and discussion. The fusion of change discovery, genetics algorithms, data mining, and computer-supported collaborative tools provide computational models of innovation and creativity. The proposed technology allows groups of participants in a creative processes to have pervasive access to the analysis of the current scenario in real time. This paper introduces such innovation technologies gathered in the DISCUS project, and summarizes the usage of DISCUS on marketing research workshops.

[1]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

[2]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .

[3]  David E. Goldberg,et al.  Discovering Deep Building Blocks for Competent Genetic Algorithms Using Chance Discovery via KeyGraphs , 2003, Chance Discovery.

[4]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[5]  David E. Goldberg,et al.  Evolutionary Computation As A Form Of Organization , 2002, GECCO.

[6]  Yukio Ohsawa,et al.  KeyGraph: automatic indexing by co-occurrence graph based on building construction metaphor , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.

[7]  Knut Holt Brainstorming—From Classics to Electronics , 1996 .

[8]  Xavier Llorà,et al.  Enhanced Innovation: A Fusion of Chance Discovery and Evolutionary Computation to Foster Creative Processes and Decision Making , 2004, GECCO.

[9]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[10]  Peter McBurney,et al.  Chance Discovery , 2003, Advanced Information Processing.

[11]  M. Weiser The Computer for the Twenty-First Century , 1991 .

[12]  Naohiro Matsumura Topic Diffusion in a Community , 2003, Chance Discovery.

[13]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[14]  Xavier Llorà,et al.  Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness , 2005, GECCO '05.