Reducing user fatigue within an interactive evolutionary design system using clustering and case-based reasoning

User fatigue is a limiting factor in interactive evolutionary design and optimization systems. This work illustrates how user fatigue arising from repetitive evaluations can be reduced by incorporating a case-based machine learning system and by clustering the population. An interactive evolutionary design system for urban furniture design is introduced and used as a test-bed for the implementation. The role of clustering within the system is described and initial results are presented. Results obtained from previous work supporting the choice of a case-based approach to machine learning are then presented and, finally, the results from a multi-user study of the performance of the case-based learning system when applied to the design of urban furniture are included.

[1]  Sung-Bae Cho,et al.  An efficient genetic algorithm with less fitness evaluation by clustering , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2]  Ashutosh Tiwari,et al.  An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization , 2007, Comput. Ind..

[3]  Ian C. Parmee,et al.  Improving problem definition through interactive evolutionary computation , 2002, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[4]  M. Rosenman.  The Generation of Form Using an Evolutionary Approach , 1997 .

[5]  Ian C. Parmee,et al.  Preferences and their application in evolutionary multiobjective optimization , 2002, IEEE Trans. Evol. Comput..

[6]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[7]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[8]  C. J. Moore,et al.  Establishing a knowledge base for bridge aesthetics , 1996 .

[9]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[10]  I. C. Parmee,et al.  INTRODUCING MACHINE LEARNING WITHIN AN INTERACTIVE EVOLUTIONARY DESIGN ENVIRONMENT , 2006 .

[11]  Peter J. Bentley,et al.  CREATIVE EVOLUTIONARY SYSTEMS , 2001 .

[12]  I. C. Parmee,et al.  USER-CENTRIC EVOLUTIONARY DESIGN , 2004 .

[13]  Hideyuki Takagi,et al.  The effect of user interaction mechanisms in multi-objective IGA , 2007, GECCO '07.

[14]  Ian C. Parmee,et al.  Integrating aesthetic criteria with evolutionary processes in complex, free-form design – an initial investigation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[15]  M. Shackelford,et al.  Collaborative evolutionary multi-project resource scheduling , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[16]  Yaochu Jin,et al.  Knowledge incorporation in evolutionary computation , 2005 .

[17]  I. C. Parmee,et al.  Interactive Evolutionary Design , 2005 .

[18]  Takeshi Furuhashi,et al.  Development of nurse scheduling support system using interactive EA , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[19]  Miguel Arias Estrada,et al.  Evolutionary Design by Computers , 2009 .

[20]  I. C. Parmee,et al.  OVERCOMING REPRESENTATION ISSUES WHEN INCLUDING AESTHETIC CRITERIA IN EVOLUTIONARY DESIGN , 2005 .

[21]  Hideyuki Takagi,et al.  Image filter design with interactive evolutionary computation , 2003 .

[22]  Hiromitsu Takagi,et al.  Interactive evolutionary computation: Cooperation of computational intel-ligent and human kansei , 1998 .

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

[24]  Azahar T. Machwe,et al.  Integrating Aesthetic Criteria with a User-centric Evolutionary System via a Component-based Design Representation , 2005 .

[25]  Sung-Bae Cho,et al.  Fashion Design Using Interactive Genetic Algorithm with Knowledge-based Encoding , 2005 .