Computational Intelligence

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Published on December 23, 2008

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Slide 1: Computational Intelligence Dr. Garrison Greenwood, Dr. George Lendaris and Dr. Richard Tymerski http://web.cecs.pdx.edu/~greenwd/ http://www.sysc.pdx.edu/faculty/Lendaris/lendaris.html http://www.ece.pdx.edu/People/Tymerski.html Evolutionary Computation (genetic algorithms, etc.), fuzzy systems and neural networks form the computational intelligence area. Active research explores the design and analysis of complex adaptive systems, reinforcement learning, intelligent control, robotics and evolvable hardware. Research is conducted in the Evolvable Systems Laboratory, Northwest Computational Intelligence Laboratory and in the Intelligent Robotics Laboratory. Students interesting in studying computational intelligence must enroll in: ECE 559 (Genetic Algorithms) ECE 555 (Neural Networks I) ECE 556 (Neural Networks II) ECE 578 (Intelligent Robotics I) ECE 579 (Intelligent Robotics II) A good mathematics background in graph theory and probability is also highly recommended. Selected Publications in Evolutionary Computation. G. Greenwood, ``Density in accessibility digraphs'', Graph Theory Notes of New York XLIX, 7-10, 2005 G. Quan, G. Greenwood and X. Hu, ``Searching for multiobjective preventive maintenance schedules: combining preferences with evolutionary algorithms'', European Journal of Operations Research (to appear) G. W. Greenwood, ``On the practicality of using intrinsic reconfiguration for fault recovery'', IEEE Transactions on Evolutionary Computation 9(4), 398-405, 2005 G. W. Greenwood, ``On the usefulness of accessibility graphs with combinatorial optimization problems'', Journal of Interdisciplinary Mathematics 8(2), 277-286, 2005 G. Greenwood, ``Intrinsic evolution of safe control strategies for autonomous spacecraft'', IEEE Transactions on Aerospace & Electronic Systems, 40(1), 236-246, 2004 G.W. Greenwood, David Hunter and Edward Ramsden, ``Fault recovery in linear systems via intrinsic evolution'', Proceedings 2004 NASA/DOD Conference on Evolvable Hardware, 115-122, 2004 G.W. Greenwood, ``Differing mathematical perspectives of genotype space in combinatorial problems: metric spaces vs pretopological spaces'', Proceedings 2004 Congress on Evolutionary Computation, 258-264, 2004 G. W. Greenwood, ``Adapting mutations in genetic algorithms using gene flow principles'', Proceedings 2003 Congress on Evolutionary Computation, 1392-1397, 2003 Research Interests in Evolutionary Computation Many real-world optimization problems are difficult to solve because, in part, they have an exponential number of possible solutions---exhaustively looking at each one to find the best solution is not feasible. The class of NP-complete problems fall into this category. We can, however, collect all possible solutions onto a hyper-surface, where each point corresponds to a unique solution. We can then design a computer algorithm that explores this hyper-surface, searching for the globally best solution. Unfortunately, this search process is not trivial because the hyper-surface often contains numerous local optima. The only hope of finding a reasonably good solution is to conduct a heuristic search. The field of evolutionary computation (EC) involves the study and development of algorithms that simulate Darwinian evolution to conduct a search. Three fundamental algorithms are the genetic algorithm, the evolution strategy, and evolutionary programming. Research Interests in Artificial Neural Networks Dr. Lendaris research interests include the development and application of massively parallel computation methodology known as neural networks or connectionist networks. Methodology development focuses on the idea of matching the structure/architecture of a network to structural relations in data of problem context. This requires developing a common mechanism for describing structure in data and structure of a network so a matching process can be possible. One approach is based on a knowledge representation formalism known as conceptual structures, and another is based on a structure representation formalism called general systems methodology (GSM) notation. Applications being pursued include pattern recognition and implementation of selected database/expert system operations. In the planning stage are control applications. Future work includes collaboration with other faculty in developing analog/digital VLSI implementations of neural networks. Selected Publication in Neural Networks R.A. Santiago, G. Lendaris, “Reinforcement Learning and the Frame Problem,” Proc. IJCNN, 2005. L. Holmstrom, R.A. Santiago, “On-Line System Identification Using Context Discernment,” Proceedings IJCNN, 2005. S. Matzner, T.T. Shannon, G. Lendaris, “Learning with Binary-Valued Utility Using Derivative Adaptive Critic Methods," Proceedings IJCNN, 2004. G. Lendaris, J. Neidhoefer, “Guidance in the Use of Adaptive Critics for Control,” Ch. 4 in Handbook of Learning and Approximate Dynamic Programming, J. Si, A.G. Barto, W.B. Powell, D. Wunsch, Eds., 97-124, 2004. R. Santiago, J. McNames, G. Lendaris, K.J. Burchiel, “Automated Method for Neuronal Spike Source Identification,” Neural Networks, Special Issue, 2003. G. Lendaris, R.A. Santiago, J. McCarthy, & M.S. Carroll, “Controller Design via Adaptive Critic and Model Reference Methods,” Proceedings of IJCNN’03, 2003. A.N. Al-Rabadi, G. Lendaris, “Artificial Neural Network Implementation Using Many-Valued Quantum Computing,” Proceedings of IJCNN, 2003. T.T. Shannon, R.A. Santiago, G. Lendaris, “Accelerated Critic Learning in Approximate Dynamic Programming via Value Templates and Perceptual Learning,” Proc. IJCNN, 2003. Research Interests in Swarm Algorithms Dr. Tymerski works on Particle Swarm Optimization and its applications in financial engineering. Particle swarm optimization (PSO) is an optimization and problem-solving approach that uses swarm intelligence. The analogy is to a swarm of insects or a school of fish. If one sees a desirable path to go (e.g., for food, protection, etc.) the rest of the swarm will be able to follow quickly from every location. In addition, each particle has some randomness of movement. This is modeled by particles in multidimensional space. Particles have positions and velocities. They communicate their knowledge to other particles.

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