Massively parallel algorithms from physics and biology

References

• Wolfram , S. 1986 . Theory and applications of cellular automata , Singapore : World Scientific .
   Google Scholar
• Bhatnagar , P. , Gross , E.P. and Krook , M.K. 1954 . A model for collision processes in gases I: Small amplitude processes in charged and neutral one-component systems . Phys. Rev. , 94 : 511
   Web of Science ®Google Scholar
• Hardy , J. and Pomeau , Y. 1972 . Thermodynamics and hydrodynamics for a model fluid . J. Math. Phys. , 13 : 1042
   Google Scholar
• Hardy , J. , Pomeau , Y. and De Pazzis , O. 1973 . Time evolution of a two-dimensional model system . J. Math. Phys. , 14 : 1746
   Web of Science ®Google Scholar
• Frisch , U. , Hasslacher , B. and Pomeau , Y. 1986 . Lattice-gas automata for the Navier–Stokes equation . Phys. Rev. Lett. , 56 : 1505
   PubMed Web of Science ®Google Scholar
• D'Humieres , D. , Lallemand , P. and Frisch , U. 1986 . Lattice gas models for 3D Hydrodynamics . Europhys. Lett. , 2 : 291
   Google Scholar
• Frisch , U. D'Humieres , D. 1987 . Lattice gas hydrodynamics in two and three dimensions . Complex Systems , 1 : 649
   Google Scholar
• Doolen , G.D. , ed. 1989 . Lattice gas methods for partial differential equations , Reading, MA : Addision-Wesley .
   Google Scholar
• Doolen G. D. Ed. Lattice gas methods for PDEs, P;hysica D 47(1/2) North-Holland 1991
   Google Scholar
• Yuanxiang , Li. , Lishan , Kang and Zhijian , Wu . 1993 . A new class of lattice gas methods . Neural Parallel and Scientific Computations , 1 : 43
   Google Scholar
• Li , Y.X. , Kang , L.S. and Chen , Y.P. 1994 . Lattice gas automata , Beijing : Tsinghua University Press .
   Google Scholar
• Mcnamara , G. and Zanetti , G. 1988 . Use of the Boltzmann equation to simulate lattice gas automata . Phys. Rev. Lett. , 61 : 2332
   PubMed Web of Science ®Google Scholar
• Higuera , F. , Succi , S. and Benzi , R. 1989 . Boltzmann approach to lattice gas simulation . Europhys. Lett. , 9 : 663
   Google Scholar
• Chen , S. , Chen , H. , Martinez , D. and Matthaeus , W.H. 1991 . Lattice Boltzmann model for simulation of magnetohydrodynamics . Phys. Rev. Lett. , 67 : 3776
   PubMed Web of Science ®Google Scholar
• Qian , Y.H. , D'Humieres , D. and Lallemand , P. 1992 . Lattice BGK models for Navier–Stokes equation . Europhys. Lett. , 17 : 479
   Google Scholar
• Chen , S. Wang , Z. 1992 . Lattice Boltzmann computational fluid dynamics in three dimensions . J. S tat. Phys. , 68 : 379
   Web of Science ®Google Scholar
• Chapman , S. and Cowling , T.G. 1970 . The mathematical theory of non-uniform gases , Cambridge : Cambridge University Press .
   Google Scholar
• Zou X. F. Li Y. X. A \D Lattice-Boltzmann Model with Energy Equation, ICPA′95 1995
   Google Scholar
• Sod , G.A. 1978 . A Survey of Several Finite Difference Methods for Systems of Non-Linear Hyperbolic Conservation Laws . J. Comp. Phys. , 27 ( 1 )
   Web of Science ®Google Scholar
• Ponce Dawson , S. , Chen , S. and Doolen , G.D. 1993 . Lattice Boltzmann computations for reaction–diffusion equations . J. Chem. Phys. , 98 : 1514
   Web of Science ®Google Scholar
• Boghosian , B. and Levermore , C.D. 1987 . A cellular automaton for Burgers' equation . Complex Systems , 1 ( 17 )
   Google Scholar
• Elton , B.H. 1991 . A lattice Boltzmann method for a two-dimensional viscous Burgers equation: computational results . Super Computing , 91 : 242 – 242 .
   Google Scholar
• Xu , Kun and Prendergast Kevin , H. 1994 . Numerical Navier–Stokes solutions from gas kinetic theory . J. Compt. Phys. , 114 ( 9 )
   Google Scholar
• Ancona , M.G. 1994 . Fully-Lagrangian and lattice-Boltzmann methods for solving systems of conservation equations . J. Compt. Phys. , 115 ( 107 )
   Google Scholar
• Yuanxiang , Li. and Lishan , Kang . 1995 . Lattice models for first order wave equations . Proceedings of Neural Parallel and Scientific Computations . 1995 .
   Google Scholar
• Bäck , T. 1994 . Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms . Proc. of First IEEE Conf on Evolutionary Computation , 1 : 57 – 62 .
   Google Scholar
• Chen , D. , Giles , C. , Sun , G. , Chen , H. , Less , Y. and Goudreau , M. . Constructive learning of recurrent neural networks . IEEE International Conference on Neural Networks . Vol. 3 , pp. 1196 – 1201 .
   Google Scholar
• Fang , K.T. and Zhang , J.T. 1993 . A New Algorithm for Calculation of Estimates of Parameters of Nonlinear Regression Modellings . Acta Mathematicae Applicatae Sinica , 16 ( 3 ) : 366 – 377 .
   Google Scholar
• Fang , K.T. , Quan , H. and Chen , Q.Y. 1988 . Applied Regression Analysis , Beijing : Science Press . (in Chinese)
   Google Scholar
• Fogel , D.B. , Fogel , L.J. and Porto , V.W. 1990 . Evolving Neural Networks . Biol.Cybern , 63 : 487 – 493 .
   Web of Science ®Google Scholar
• Frean , M. 1990 . The upstart algorithm: A method for constructing and training feedforward neural networks . Neural Computation , 2 : 198 – 209 .
   Web of Science ®Google Scholar
• Goldberg , D.E. . Sizing populations for serial and parallel algorithms . Proc. 3rd Int. Conf. on G.A and Applications . San Mateo, Calif : Morgan Kaufman .
   Google Scholar
• Goldberg , D.E. 1989 . Genetic Algorithms in Search, Optimization and Machine Learning , Addison Wesley .
   Google Scholar
• Goldberg , D.E. and Deb , K. 1991 . A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , Edited by: Rawlins , G. 69 – 93 . Foundations of Genetic Algorithms .
   Google Scholar
• Holland , J.H. 1975 . Adaptation in Natural and Artificial Systems , The University of Michigan Press .
   Google Scholar
• Kolmogorov , A.N. 1957 . On the representation of continuous functions of many variables by superposition of continuous functions of one function and addition . Dokl Akad VSSR , 14 : 953 – 956 .
   Google Scholar
• Koza , J.R. 1992 . Genetic Programming: On the Programming ofCornputers by Means of Natural Selection , Cambridge : MIT Press .
   Google Scholar
• Linhart , H. and Zucchini , W. 1986 . Model Selection , New York : John Wiley & Sons .
   Google Scholar
• Maniezzo , V. 1994 . Genetic Evolution of the Topology and Weight Distribution of Neural Networks . IEEE Trans.on Neural Networks , 5 ( 1 ) : 39 – 53 .
   PubMed Web of Science ®Google Scholar
• Michalewicz , Z. 1992 . Genetic Algorithms + Data Structures = Evolution Programs , Berlin : Springer-Verlag .
   Google Scholar
• Nash , J.C. and Walker-Smith , M. 1987 . Nonlinear Parameter Estimation , Basel, New York : Marcel Dekker .
   Google Scholar
• Pan , Z.J. , Kang , L.S. , He , J. and Liu , Y. 1995 . “ An Evolutionary Approach to Adaptive Model-Building ” . In Lecture Notes in Artificial Intelligence No.956 , Edited by: Yao , Y. Berlin : Springer-Verlag .
   Google Scholar
• Pan , Z.J. , Chen , Y.J. , Kang , L.S. and Zhang , Y.T. 1995 . Parameter Estimation by Genetic Algorithms for Nonlinear Regression , Edited by: Liu , G.Z. Vol. 2 , 946 – 953 . Singapore : World Scientific . Optimization Techniques and Applications
   Google Scholar
• Pan Z.J. Kang L.S. Evolving Both the Topology and weights of Neural Networks Wuhan, 430072, P.R. Technical Report, Institute of Softare Engineering, Wuhan University China 1995
   Google Scholar
• Poggio , T. and Girosi , F. 1990 . Networks for Approximation and Learning , 78 ( 9 ) : 1481 – 1497 . Proceeding of the IEEE
   Google Scholar
• Polak , E. 1971 . Computational Methods in Optimization - a unified approach , New York : Academic Press .
   Google Scholar
• Ratkowsky , D.A. 1983 . Nonlinear Regression Modeling - a unified practical approach , Marcel Dekker .
   Google Scholar
• Azimi-Sadjadi , M.R. , Sheedvash , S. and Trujillo , F.O. 1993 . Recursive dynamic node creation in multilayer neural networks . IEEE Transaction on Neural Networks , 4 ( 2 ) : 242 – 256 .
   PubMed Web of Science ®Google Scholar
• Syswerda , G. 1991 . Uniform crossover in genetic algorithms . Proc.of the 4th Int.Conf.on Genetic Algorithms . 1991 . Edited by: Belew , R. and Booker , L. pp. 2 – 9 .
   Google Scholar
• Whitley , D. 1989 . The Genitor Algorithm and Selection Pressure: Why Rank-based Allocation of Reproductive Trials is Best . Proc.of the Third Int.Conf.on Genetic Algorithms . 1989 . Edited by: Schaffer , J.D. pp. 116 – 121 .
   Google Scholar
• Yao , X. 1993 . A Review of Evolutionary Artificial Neural Networks . International Journal of Intelligent Systems , 8 ( 4 ) : 539 – 567 .
   Web of Science ®Google Scholar
• Booker , L. , Goldberg , D. and Holland , J. 1989 . Classifier and genetics algorithms . Artificial Intelligence , 40 : 235 – 282 .
   Web of Science ®Google Scholar
• Conrad , M. 1983 . Adaptability , New York : Plenum Press .
   Google Scholar
• Davis , L. 1990 . The Handbook of Genetic Algorithms , Van Nostrand Reinhold .
   Google Scholar
• Jun , He . 1995 . Convergence of genetic evolution algorithms for optimization . Parallel Algorithms and Applications , 5 : 37 – 56 .
   Google Scholar
• Jun , He . 1995 . Evolutionary computation from ecosystem , Wuhan : Wuhan University . Ph.D.Thesis, Department of Computer Science
   Google Scholar
• Hillis , W.D. 1990 . Convolution parasites improve simulated evolution as an optimization procedure . Physica D , 42 : 228 – 234 .
   Google Scholar
• Hogeweg , P. 1994 . Multilevel evolution: replications and evolution of diversity . Physica D , 75 : 275 – 291 .
   Google Scholar
• Husbands , P. 1994 . An ecosystem model for integrated production planning . Int.Journal Integrated Manufacturing , 75
   Google Scholar
• J⊘Rgensen , S.E. 1988 . Fundamentals of ecological modeling , Elsevier .
   Google Scholar
• May , R.A. , ed. 1976 . Theoretical ecology: principles and applications , Oxford : Blackwell scientific Publications .
   Google Scholar
• MüHlenbein , H. , Sehomisch , M. and Born , J. 1991 . Parallel genetic algorithms as function optimizer . Parallel Computing , 17 : 269 – 279 .
   Google Scholar
• Mühlenbein , H. “ Evolution in time and space: the parallel genetic algorithm ” . In Foundations of Genetic Algorithms , Edited by: Rawlins , G.J.E. 316 – 327 . Morgan Kaufmann .
   Google Scholar
• Simon , H. 1981 . The sciences of the artificial , Cambridge, MA : The MIT Press .
   Google Scholar