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|a Karaman, Sertac
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|a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Karaman, Sertac
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|a Frazzoli, Emilio
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|a Shima, Tal
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|a Frazzoli, Emilio
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|a A Process Algebra Genetic Algorithm
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|b Institute of Electrical and Electronics Engineers (IEEE),
|c 2013-10-21T14:30:51Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/81446
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|a A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulated by evolutionary operators. The evolutionary operators of crossover and mutation are formally defined using the process algebra methodology, along with specific algorithms needed for their execution. The viability of the approach is investigated using simulations and the effectiveness of the algorithm is shown in small, medium, and large scale problems.
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|a United States. Air Force Office of Scientific Research (Michigan/AFRL Collaborative Center in Control Science Grant FA 8650-07-2-3744)
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|a United States. Air Force Office of Scientific Research (Grant FA8655-09-1-3066)
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|a en_US
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|a Article
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|t IEEE Transactions on Evolutionary Computation
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