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Details robustness, stability, and performance of Evolutionary Algorithms in dynamic environments
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Dr. Morrison has been at Mitretek Systems for four years as a Senior Manager and Fellow. He currently serves as an advisor to U.S. government officials regarding advanced software development projects. Previously, Dr. Morrison was Chief Scientist for the SWL division at GRC International, where he was responsible for product development and innovation involving new techniques and applications in the areas of data visualization, computational intelligence, machine learning, and high-speed decision support systems. His accomplishments at GRCI include the creation of a novel genetic-algorithm based decision-support system for commodity traders, development of a method for integrating quantitative and qualitative information for a U.S. government agency, and the framework design for a commercial software-based intelligent agent for use by the Defense Advanced Research Projects Agency. Before joining GRCI, Dr. Morrison was Director of Software Engineering at Hughes Training, Inc., developing high-fidelity, real-time flight simulators for U.S. and foreign military customers.
Dr. Morrison has presented multiple papers at major internatinal conferences on Evolutionary Compuation, has served as the Technical Director for the Software Program Manager's Network and is a past member of the Airlie Software Council. He was an invited speaker at the initial meeting of the Narional Software Alliance in 1998 and at the AIE-sponsored Annual Conference on Software Metrics. He holds a B.S. in Aeronautical and Astronautical Engineering from Purdue University, an M.B.A. from Southern Illinois University, and a Ph.D. in Information Technology from George Mason University.
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Designing Evolutionary Algorithms for Dynamic Environments
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Designing Evolutionary Algorithms for Dynamic Environments
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Designing Evolutionary Algorithms for Dynamic Environments (Hardcover)
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Hardcover. Zustand: new. Hardcover. The robust capability of Evolutionary Algorithms (EAs) to find solutions to difficult problems has permitted them to become the optimization and search techniques of choice for many practical static problems. Despite this success in many different environments, EAs are often prone to failure when subjected to even small changes in the problem. Effective solutions for many real-world engineering and economic problems require systems that adapt to changes over time. This book addresses the issues involved in the design of EAs that successfully operate in dynamic environments without human intervention, and provides a method for creating EAs for these environments. The robust capability of evolutionary algorithms (EAs) to find solutions to difficult problems has permitted them to become popular as optimization and search techniques for many industries. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783540212317
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Designing Evolutionary Algorithms for Dynamic Environments
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