http://dspace.mit.edu:80 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. http://hdl.handle.net/1721.1/46322 The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization Jackson, Daniel Estler, H.-Christian Rayside, Derek This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top of the non-optimizing relational constraint solver Kodkod. We compare the performance of this new algorithm against two algorithms from the literature [Gavanelli 2002, Lukasiewycz et alia 2007, Laumanns et alia 2006]) on three micro-benchmark problems (n-Queens, n-Rooks, and knapsack) and on two aerospace case studies. Results indicate that the new algorithm is better for the kinds of many-objective problems that our aerospace collaborators are interested in solving. The new algorithm returns Pareto-optimal solutions as it computes. Thu, 02 Jul 2009 22:58:59 GMT http://hdl.handle.net/1721.1/46321 12.007 Geobiology, Spring 2003 Sachs, Julian P. (Julian Perelman) Summons, Roger E. The interactive Earth system: biology in geologic, environmental and climate change throughout Earth history. Since life began it has continually shaped and re-shaped the atmosphere, hydrosphere, cryosphere and the solid earth. Subject introduces the concept of "life as a geological agent" and examines the interaction between biology and the earth system during the roughly 4 billion years since life first appeared. Topics include the origin of the solar system and the early Earth atmosphere; the origin and evolution of life and its influence on climate up through and including the modern age and the problem of global warming; the global carbon cycle; and astrobiology. Thu, 29 May 2003 22:58:59 GMT http://hdl.handle.net/1721.1/46320 6.867 Machine Learning, Fall 2002 Jaakkola, Tommi S. (Tommi Sakari) Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks. From the course home page: Course Description 6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered. Thu, 28 Nov 2002 22:58:59 GMT http://hdl.handle.net/1721.1/46319 3.225 Electronic and Mechanical Properties of Materials, Summer 2002 Gibson, Lorna J. Tuller, Harry L. Fitzgerald, Eugene Electrical, optical, magnetic, and mechanical properties of metals, semiconductors, ceramics and polymers. Discussion of roles of bonding, structure (crystalline, defect, energy band and microstructure) and composition in influencing and controlling physical properties. Case studies drawn from a variety of applications including semiconductor diodes, optical detectors, sensors, thin films, biomaterials, composites, and cellular materials. Mon, 29 Oct 2001 22:58:59 GMT