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Game Theory Approach to Engine Performance Optimization
Technical Paper
2008-01-0871
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Genetic Algorithms have proved to be very useful as global search methods for multi-dimensional optimization problems. One drawback, however, is that they are inefficient from the point of view of the number of function evaluations. This paper presents a two phase approach to optimization, using Game Theory in an initial step which provides a family of designs which are close to the Pareto frontier. The starting population for the genetic algorithm is then selected from the non-dominated designs produced in the first phase. This ensures that the genetic algorithm starts with a population of points which are already optimized to a large degree.
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Authors
Citation
Fateh, N., Parashar, S., and Silvestri, J., "Game Theory Approach to Engine Performance Optimization," SAE Technical Paper 2008-01-0871, 2008, https://doi.org/10.4271/2008-01-0871.Also In
References
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- modeFRONTIER version 3.2 Documentation www.esteco.com