Need a paper on Genetic Algorithm with Neural Networks including the algorithm and its procedure?
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Your best bet is to go to your university library and look it up in the math and computer science section. There are books on this and if your library doesn't have them you can do an interlibrary loan to get them. To look extra smart, find an example in the literature and reference their work. Look up Peter Jurs' papers from Penn State University. They use genetic algorithms and neural networks to predict physical properties of molecules
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The Hungarian algorithm is a combinatorial optimization algorithm which solves assignment problems in polynomial time. The first version, known as the Hungarian method, was invented and published by Harold Kuhn in 1955. This was revised by James Munkres in 1957, and has been known since as the Hungarian algorithm, the Munkres assignment algorithm, or the Kuhn-Munkres algorithm.\n\nTheory\nThe algorithm developed by Kuhn was largely based on the earlier works of two other Hungarian mathematicians: Denes Kőnig and Jenő Egerváry. The great advantage of Kuhn’s method is that it is strongly polynomial. The main idea of the algorithm is that it combines two separate parts in Egervery’s proof (computing a deficient set and revising the current π) into one. The procedure of covering the prime zeros can be implemented in a flow network, and automated by means of the Ford-Fulkerson algorithm. The n×m matrix is transformed in a G = (U,V) bipartite graph, with I/O capacity equal to 1. Each arc joining the nodes of the flow network represents a zero in the cost matrix. After the maximum flow is obtained, the affected arcs represent the prime zeros. The minimum cut obtained by Ford-Fulkerson automates the process of covering the independent zeros. Each node that gets "cut" on the max-flow graph represents a covered row or line on the cost matrix. However, in the Hungarian method, the assignment can be easily found on the cost matrix, yet the expansion into a max-flow sub-problem is a useful method in more complex scenarios.\n\nModeling\nThe algorithm models an assignment problem as a n×m cost matrix, where each element represents the cost of assigning the nth worker to the mth job. By default, the algorithm performs minimization on the elements of the matrix; hence in the case of a price-minimization problem, it is sufficient to begin Gaussian elimination to make zeros appear (at least one zero per line and per column). However, in the case of a profit-maximization problem, the cost matrix needs to be modified so that minimization of its elements results in maximizing the original cost values. In an infinite-cost problem, the initial cost matrix can be re-modelled by subtracting every element of each line from the maximum value of the element that line (or column respectively). In a finite-cost problem, all the elements are subtracted from the maximum value of the whole matrix.\n\nfor more details see link:\nhttp://en.wikipedia.org/wiki/Hungarian_algorithm
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Genetics. And Darwins theory.
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well.. here is a really wonderful book for this :\n\nDesign and Analysis of algorithms by Anany Levitin.. try getting this book.\n\nAnd : \nhere's a dictionary of algorithms:\n\nhttp://www.nist.gov/dads/\nhere, each and every algorithm's details have been given. And links to other sites, where simulations of algorithms are put up as JAVA APPLETS\n\nfor eg:\nhere's a link to quick sort simultaion using java applet, which i got from NIST . gov :\n\nhttp://www.cosc.canterbury.ac.nz/people/mukundan/dsal/QSort.html\n\nN JOY ; )
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Prolog or Lisp. Machine learning\nMore detail please.\nI'm more for Java.\nBut most likely you are looking for the toolkit From MATLAB(tm) the first link.
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Hey Alex, There are several area's of AI and in each there are  several "good" AI algorithms.  If your simply looking for something interersting to study I recommend looking into searching algorithms.  More specifically a very interesting introductory algorithm you could look at is The minimax algorithm with alpha-beta pruning.  Not only is this a fun algorithm to work through, you will learn several usefull ideas that apply to fields from Artificial Intelligence through to Computational Genetics.  There are of course hundreds more but this was my one of my first AI algorithms and I learned a lot from it.  If you are looking for a good introductory text to Artificial Intelligence I recommend "Artificial Intelligence: A modern approach" by Stuart Russen and Peter Norvig.  Hope this helps and good luck.
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1- Arbib, M. A. (1969). Theories of Abstract Automata. Englewood\nCliffs, NJ: Prentice-Hall.\n2- Arbib, M. A. (1987). Brains, Machines and Mathematics. Second\nedition. New York: Springer.\n3- Arbib, M. A., Ed. (1995). The Handbook of Brain Theory and Neural\nNetworks. Cambridge, MA: MIT Press\n3- Chomsky, N. (1963). Formal properties of grammars. In R. D.\nLuce, R. R. Bush, and E. Galanter, Eds., Handbook of Mathematical\n4- Handbook of Mathematical Psychology, vol. 2. New York:\nWiley,  Minsky, M. L. (1967). Computation: Finite and Infinite Machines.\nEnglewood Cliffs, NJ: Prentice-Hall.\n5- Post, E. L. (1936). Finite combinatory processes—formulation I.\nJournal of Symbolic Logic 1: 103–105.\n6- Seidenberg, M. (1995). Linguistic morphology. In M. A. Arbib,\nEd., The Handbook of Brain Theory and Neural Networks.\nCambridge, MA: MIT Press.\n6- Shannon, C. E., and J. McCarthy, Eds. (1956). Automata Studies.\nPrinceton: Princeton University Press.\n7- Turing, A. M. (1936). On computable numbers. Proc. London\nMath. Soc. \n\nI can't work the format when I post my answer, but I reposted the answer with numbers so it can be easy for you to look at.
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As usual, Google knows.\n\nGoogle says:\n\n"Did you mean: neural networks"
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Try this paper ...\nhttp://www.citebase.org/fulltext?format=application%2Fpdf&identifier=oai%3AarXiv.org%3Anucl-th%2F0403083
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I found the Wikipedia's article on Newton's method to be very good:\n\nhttp://en.wikipedia.org/wiki/Newton's_method
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There is a lot of information here, plus a large bibliography:\n\nhttp://www.dsi.unifi.it/ANNxDAR/
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A neural network is an interconnected group of biological neurons. In modern usage the term can also refer to artificial neural networks, which are constituted of artificial neurons. Thus the term 'Neural Network' specifies two distinct concepts:\n\n   1. A biological neural network is a plexus of connected or functionally related neurons in the peripheral nervous system or the central nervous system. In the field of neuroscience, it most often refers to a group of neurons from a nervous system that are suited for laboratory analysis.\n   2. Artificial neural networks were designed to model some properties of biological neural networks, though most of the applications are of technical nature as opposed to cognitive models.
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http://www.cs.ucdavis.edu/~haungs/paper/node11.html
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binary generation or backtracking techniques
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Yes:  theory of computational systems.
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U can use either Kruskal's algorithm or prims algorithm. More information about these algorithm u can find on internet.\n\nPrims algorithm is based on adjacency matrix.
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I think you are talking about that book:\n\nOldrich Vasicek, "A test for normality based on sample entropy," Journal of the Royal Statistical Society, Series B, vol. 38, no. 1, pp. 54--59, 1976.\n\nI couldn't find a copy of this article, but it is easy to find a lot of other articles that are refer to it.\nHope, one of them will help you.\n\nAlso I strongly recommend to use a Citeseer scientific article database (2nd attached link). It usually contains a lot of information.