Decision Support Methods

This section presents a summary of the main concepts on which evolutionary algorithms are based. First, the operating principle of Genetic Algorithms (GAs) is explained and their main parts and their evolution parameters described. Next, a description of Cultural Algorithms (CAs) is presented and its main components are pointed out.

[1]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[2]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[3]  Marley M. B. R. Vellasco,et al.  Electric load forecasting: evaluating the novel hierarchical neuro-fuzzy BSP model , 2004 .

[4]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[5]  Mel Slater,et al.  Computing Dynamic Changes to BSP Trees , 1992, Comput. Graph. Forum.

[6]  R. Bellman.  Dynamic programming. , 1957, Science.

[7]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[8]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[11]  Steven K. Feiner,et al.  Near real-time shadow generation using BSP trees , 1989, SIGGRAPH '89.

[12]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[17]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[18]  Lee A. Feldkamp,et al.  Decoupled extended Kalman filter training of feedforward layered networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[19]  John R. Koza,et al.  Genetic programming 2 - automatic discovery of reusable programs , 1994, Complex Adaptive Systems.

[20]  M. Ghiselin,et al.  Coevolution: Genes, Culture, and Human Diversity , 1991, Politics and the Life Sciences.

[21]  K. Yamada.  Probability-possibility transformation based on evidence theory , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[22]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[23]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[24]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[25]  S. Moral,et al.  On the concept of possibility-probability consistency , 1987 .

[26]  Robert G. Reynolds,et al.  Res , 2004, Encyclopedia of Early Modern History Online.

[27]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[28]  W. Pedrycz,et al.  An introduction to fuzzy sets : analysis and design , 1998 .

[29]  M.M.B.R. Vellasco,et al.  Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[30]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[31]  Marley M. B. R. Vellasco,et al.  Hierarchical Neuro-Fuzzy Systems Part II , 2009, Encyclopedia of Artificial Intelligence.

[32]  On the concept of possibility-probability consistency measure-general cases , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[33]  Ramon E. Moore.  Methods and applications of interval analysis , 1979, SIAM studies in applied mathematics.

[34]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[35]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[36]  Jong-Hwan Kim,et al.  Genetic quantum algorithm and its application to combinatorial optimization problem , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[37]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[38]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

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

[40]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[41]  Petri Vuorimaa,et al.  Fuzzy self-organizing map , 1994 .

[42]  H. C. Chen,et al.  Uncertainties are Better Handled by Fuzzy Arithmetic (1) , 1990 .

[43]  Angelo Marcello Anile,et al.  Implementing fuzzy arithmetic , 1995 .

[44]  Lotfi A. Zadeh,et al.  Fuzzy Logic for Business, Finance, and Management , 1997, Advances in Fuzzy Systems - Applications and Theory.

[45]  Ronald E. Giachetti,et al.  A parametric representation of fuzzy numbers and their arithmetic operators , 1997, Fuzzy Sets Syst..

[46]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[47]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[48]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[49]  D. Dubois,et al.  Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms , 1983 .

[50]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[51]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[52]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[53]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[54]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[55]  M. Pacheco,et al.  Quantum-Inspired Evolutionary Algorithm for Numerical Optimization , 2006 .