Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Self-Organizing Maps
Visual Explorations in Finance
Visual Explorations in Finance
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Niching and Elitist Models for MOGAs
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Visualization and data mining of Pareto solutions using self-organizing map
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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A large-scale, real-world application of Evolutionary Multi- Criterion Optimization (EMO) is reported in this paper. The Multidisciplinary Design Optimization among aerodynamics, structures and aeroelasticity for the wing of a transonic regional jet aircraft has been performed using high-.delity models. An Euler/Navier-Stokes (N-S) Computational Fluid Dynamics (CFD) solver is employed for the aerodynamic evaluation. The NASTRAN, a commercial software, is coupled with a CFD solver for the structural and aeroelastic evaluations. Adaptive Range Multi-Objective Genetic Algorithm is employed as an optimizer. The objective functions are minimizations of block fuel and maximum takeo. weight in addition to di.erence in the drag between transonic and subsonic .ight conditions. As a result, nine non-dominated solutions have been generated. They are used for tradeo. analysis among three objectives. One solution is found to have one percent improvement in the block fuel compared to the original geometry designed in the conventional manner. All the solutions evaluated during the evolution are analyzed by Self-Organizing Map to extract key features of the design space.