Measuring Human Intelligence by Applying Soft Computing Techniques: A Genetic Fuzzy Approach

The chapter focuses on Genetic-Fuzzy Rule Based Systems of soft computing in order to deal with uncertainty and imprecision with evolving nature for different domains. It has been observed that major professional domains such as education and technology, human resources, psychology, etc, still lack intelligent decision support system with self evolving nature. The chapter proposes a novel framework implementing Theory of Multiple Intelligence of education to identify students’ technical and managerial skills. Detail methodology of proposed system architecture which includes the design of rule bases for technical and managerial skills, encoding strategy, fitness function, cross-over and mutation operations for evolving populations is presented in this chapter. The outcome and the supporting experimental results are also presented to justify the significance of the proposed framework. It concludes by discussing advantages and future scope in different domains.

[1]  Uzay Kaymak,et al.  A fuzzy logic based trading system , 2007 .

[2]  T. Armstrong,et al.  Multiple Intelligences: Seven Ways to Approach Curriculum. , 1994 .

[3]  Wen-Lian Hsu,et al.  Question Classification in English-Chinese Cross-Language Question Answering: An Integrated Genetic Algorithm and Machine Learning Approach , 2007, 2007 IEEE International Conference on Information Reuse and Integration.

[4]  S. Selvakani Kandeeban,et al.  Integrated Intrusion Detection System Using Soft Computing , 2010, Int. J. Netw. Secur..

[5]  Gurpreet Kaur,et al.  Assessment of Multiple Intelligence among Young Adolescents (12-14 Years) , 2008 .

[6]  Carlos Andrés Peña-Reyes,et al.  A Genetic-Fuzzy System Approach to Control a Model of the HIV Infection Dynamics , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[7]  Ajith Abraham.  i-Miner: a Web usage mining framework using hierarchical intelligent systems , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[8]  Alex Fraser.  Simulation of genetic systems , 1962 .

[9]  S Hoogendoorn-Lanser,et al.  TRAVEL CHOICE BEHAVIOR IN PUBLIC TRANSPORT NETWORKS: A FUZZY GENETIC APPROACH , 1999 .

[10]  Toni Noble,et al.  Integrating the Revised Bloom's Taxonomy with Multiple Intelligences: A Planning Tool for Curriculum Differentiation , 2004, Teachers College Record: The Voice of Scholarship in Education.

[11]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[12]  E. González,et al.  The Logistic Decision Making in Management Accounting with Genetic Algorithms and Fuzzy Sets , 2000 .

[13]  Pedro Isasi Viñuela,et al.  Evolutionary rule-based system for IPO underpricing prediction , 2005, GECCO '05.

[14]  Khairul A. Rasmani,et al.  Data-driven fuzzy rule generation and its application for student academic performance evaluation , 2006, Applied Intelligence.

[15]  Masoud Nikravesh,et al.  Web Intelligence: Web-Based BISC Decision Support System (WBISC-DSS) , 2003 .

[16]  Rajendra Akerkar,et al.  EVOLVING RULES USING GENETIC FUZZY APPROACH - AN EDUCATIONAL CASE STUDY , 2011 .

[17]  Manuel Ojeda-Aciego,et al.  Fuzzy Logic, Soft Computing, and Applications , 2009, IWANN.

[18]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[19]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[20]  Mohammad Hamiruce Marhaban,et al.  Analysis and Performance Evaluation of PD-like Fuzzy Logic Controller Design Based on Matlab and FPGA , 2010 .

[21]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[22]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[23]  Rajendra Akerkar,et al.  An Automatic Evolution of Rules to Identify Students’ Multiple Intelligence , 2011 .

[24]  Tzung-Pei Hong,et al.  A Genetics-Based Approach to Knowledge Integration and Refinement , 2001, J. Inf. Sci. Eng..

[25]  Ajith Abraham,et al.  EvoPOL: a framework for optimising social regulation policies , 2006, Kybernetes.

[26]  Robert J. Sternberg,et al.  The Theory of Successful Intelligence , 1999 .

[27]  C. Spearman.  General intelligence Objectively Determined and Measured , 1904 .

[28]  Robert Plomin,et al.  Intelligence: genetics, genes, and genomics. , 2004, Journal of personality and social psychology.

[29]  S. N. Sivanandam,et al.  Introduction to genetic algorithms , 2007 .