Rasheed, Khaled - Department of Computer Science, University of Georgia

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Rasheed, Khaled - Department of Computer Science, University of Georgia

Effect of Global Parallelism on the Behavior of a Steady State Genetic Algorithm for Design Optimization
AIAA 992108 Three Dimensional Optimization
To appear, AIAA Journal of Propulsion and Power flCopyright M. Blaize, D. Knight and K. Rasheed, 1998
Informed operators: Speeding up geneticalgorithmbased design optimization using reduced models
Figure 12: Mission profile Coefficient
Comparison of Methods for Developing Dynamic Reduced Models for Design Optimization
An Adaptive Penalty Approach for Constrained GeneticAlgorithm Optimization
Improving GA Convergence Using Guided Crossover Khaled Rasheed
An IncrementalApproximateClustering Approach for Developing Dynamic Reduced Models for Design Optimization
Using CaseBased Learning to Improve GeneticAlgorithmBased Design Optimization
Automated Standard Cell Synthesis using Genetic Algorithms
Comparison of Methods for Using Reduced Models to Speed Up Design Optimization
Learning to be Selective in GeneticAlgorithmBased Design Optimization
Adaptation of Genetic Algorithms for Engineering Design Optimization
Effect of Global Parallelism on a Steady State GA 1 Brian D. Davison Khaled Rasheed
0.0 0.2 0.4 0.6 0.8 1.0 1.2 Figure 12: Total pressure recovery distribution along the axis
A Comparison of Genetic Algorithm Methods in Aerial Spray Deposition Management
Optimization by Searching a Tree of Populations Louis Steinberg
Submitted to the AIAA Journal of Aircraft on April 1, 1996 High Performance Supersonic Missile Inlet
A Genetic Algorithm for Continuous Design Space Search
Guided Crossover: A New Operator for Genetic Algorithm Based Optimization
Comparison of Methods for Using Reduced Models to Speed Up Design Optimization
GADO: A GENETIC ALGORITHM FOR CONTINUOUS DESIGN OPTIMIZATION
Guided Crossover: A New Operator for Genetic Algorithm Based
Optimization by Searching a Tree of Populations Louis Steinberg
Using Singular Value Decomposition to Improve a Genetic Algorithm's Performance