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TitleEvolutionary Algorithms [electronic resource] / edited by Lawrence David Davis, Kenneth De Jong, Michael D. Vose, L. Darrell Whitley
ImprintNew York, NY : Springer New York : Imprint: Springer, 1999
Connect tohttp://dx.doi.org/10.1007/978-1-4612-1542-4
Descript X, 293 p. online resource

SUMMARY

This IMA Volume in Mathematics and its Applications EVOLUTIONARY ALGORITHMS is based on the proceedings of a workshop that was an integral part of the 1996-97 IMA program on "MATHEMATICS IN HIGH-PERFORMANCE COMPUTING." I thank Lawrence David Davis (Tica Associates), Kenneth De Jong (Computer Science, George Mason University), Michael D. Vose (Computer Science, The University of Tennessee), and L. Darrell Whitley (Computer Science, Colorado State University) for their excellent work in organizing the workshop and for editing the proceedings. Further appreciation is exยญ tended to Donald G. Truhlar (Chemistry and Supercomputing Institute, University of Minnesota) who was also one of the workshop organizers. In addition, I also take this opportunity to thank the National Science Foundation (NSF), Minnesota Supercomputing Institute (MSI), and the Army Research Office (ARO), whose financial support made the workshop possible. Willard Miller, Jr., Professor and Director v PREFACE The IMA Workshop on Evolutionary Algorithms brought together many of the top researchers working in the area of Evolutionary Comยญ putation for a week of intensive interaction. The field of Evolutionary Computation has developed significantly over the past 30 years and today consists a variety of subfields such as genetic algorithms, evolution strateยญ gies, evolutionary programming, and genetic programming, each with their own algorithmic perspectives and goals


CONTENT

Genetic algorithms as multi-coordinators in large-scale optimization -- Telecommunication network optimization with genetic algorithms: A decade of practice -- Using evolutionary algorithms to search for control parameters in a nonlinear partial differential equation -- Applying genetic algorithms to real-world problems -- An overview of evolutionary programming -- A hierarchical genetic algorithm for system identification and curve fitting with a supercomputer implementation -- Experiences with the PGAPack parallel genetic algorithm library -- The significance of the evaluation function in evolutionary algorithms -- Genetic algorithm optimization of atomic clusters -- Search, binary representations and counting optima -- An investigation of GA performance results for different cardinality alphabets -- Genetic algorithms and the design of experiments -- Efficient parameter optimization based on combination of direct global and local search methods -- What are genetic algorithms? A mathematical prespective -- Survey of projects involving evolutionary algorithms sponsored by the Electric Power Research Institute


Computer science Artificial intelligence Computer Science Artificial Intelligence (incl. Robotics)



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