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ATI's Neural and Evolutionary Computation course

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Summary:

    Technical Training Short On Site Course Quote

      There have been tremendous advances in the technologies of artificial neural networks and evolutionary computation for addressing difficult problems. These technologies have been successfully applied to signal and image processing, logistics, tactics optimization, fire control, data compression, antenna design, and other problems. Many potential users of these technologies are hindered by the absence of a suitable overview giving an integrated development that links neural and evolutionary computation for synergistic effects. The objectives of this three-day course are a) to give a practical introduction to the use of neural and evolutionary computation in real-world problems, b) indicate when these techniques may be most appropriately applied, c) to provide a framework that enables the student to be able to put the essential elements of these techniques into practice.

      The book Evolutionary Computation: The Fossil Record, edited by Dr. David B. Fogel, and a complete set of course notes will be given to each participant. The book is the result of four years of work documenting the development and current state of the art in evolutionary computation. Introductory computer programs illustrating some of the basic procedures will also be distributed.

    Tuition:

    Instructors:

      Dr. David B. Fogel is Executive Vice President and Chief Scientist of Natural Selection, Inc. He has a Ph.D. in Engineering Sciences from the University of California at San Diego. Dr. Fogel has 14 years of experience in applying evolutionary algorithms to complex real-world problems in industry, medicine, and defense. He is the author of over 125 papers in journal and conferences, the author and editor of two books on evolutionary computation "Evolutionary Computation: Toward a New Philosophy of Machine Intelligence," and "Evolutionary Computation: The Fossil Record," He is the founding Editor-in-Chief of the IEEE Transactions on Evolutionary Computation. Dr. Fogel also served as associate editor for the IEEE Transactions on Neural Networks, and is on the editorial boards of Fuzzy Sets & Systems, Information Sciences, and the Journal of Advanced Computational Intelligence.

      Mr. Bill Porto is Vice President of Natural Selection, Inc. He has over 15 years of experience with neural and evolutionary computation, with an particular emphasis in defense-related applications. Mr. Porto is an Associate Editor for the IEEE Transactions on Evolutionary Computation, and was the General Chairman of the 7th Annual Conference on Evolutionary Programming. He has been a member of ONR Panel on Neural Networks and the organizing committees for the 1988 and 1989 International Joint Conferences on Neural Networks. Mr. Porto has numerous publications in conferences and journals.

      Dr. Peter J. Angeline is Senior Staff Scientist of Natural Selection, Inc. Dr. Angeline holds a Ph.D. in Computer and Information Sciences from the Ohio State University. He has 10 years of experience applying neural and evolutionary algorithms to problems in image processing and combinatorial optimization. Dr. Angeline is an editor of two books ("Advances in Genetic Programming II" and "Advances in Genetic Programming III" from MIT Press). He serves as an Associate Editor for the IEEE Transactions on Evolutionary Computation. He has served as an Associate Editor for the IEEE Transactions on Neural Networks, and was a board member of the International Society for Genetic Algorithms and is Past-President of the Evolutionary Programming Society. He was also the General Chairman for the 1999 Congress on Evolutionary Computation.

      Contact these instructors (please mention course name in the subject line)

    What you will learn:

    • Fundamental concepts in neural networks and evolutionary computation.
    • How to compare and identify the most appropriate neural and evolutionary algorithms.
    • How to implement neural networks and evolutionary algorithms for your own problems.
    • How to optimize performance.
    • Lessons learned and pitfalls to avoid.
    • Emerging trends in applications.
    Course Outline:

    1. Foundations of Artificial Neural Systems. A review of the basics of neural processing elements, threshold functions, different topologies, memory, learning, stability, and the convergence of neural systems.

    2. Alternative Implementations of Neural Networks. A historical perspective and description of neural architectures for data processing including multilayer perceptrons, Adaline/Madaline, competitive learning, recurrent connections, cascade-correlation, radial basis functions.

    3. Applications of Neural Networks. An overview of applications of neural networks with particular emphasis on problems in signal and image processing, pattern classification, forecasting, and control.

    4. Foundations of Evolutionary Computation. Fundamentals of evolutionary algorithms including the use of a population of contending solutions, random variation, and selection iterated over successive generations.

    5. Alternative Implementations of Evolutionary Computation. A historical perspective and description of evolutionary algorithms including genetic algorithms, evolutionary programming, genetic programming, classifier systems, evolution strategies, and artificial life.

    6. Applications of Evolutionary Computation. An overview of applications of evolutionary computation in problems in signal and image processing, pattern classification, forecasting, and control.

    7. Hybrid Methods of Neural and Evolutionary Computation. How evolutionary computation can be used to assist in the training and design of neural networks at multiple levels including weights, connections, and the number and types of neurons.

    8. Alternative optimization methods. Which procedures are appropriate for your problems of interest. Classic methods compared with neural and evolutionary computation.

    9. Case Studies of Neural and Evolutionary Computation. In-depth treatment of applications to real-world problems with emphasis in signal processing, image analysis, fire control, and the design of intelligently interactive computer-generated forces.

    10. Emerging Technology and Future Trends.

    
    
    Tuition:

      Tuition for this three-day course is $1290 per person at one of our scheduled public courses. Onsite pricing is available. Please call us at 410-956-8805 or send an email to ati@aticourses.com.

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