Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Journal of Global Optimization
Metamodel-Assisted Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A framework for adaptive execution in grids
Software—Practice & Experience
Faster convergence by means of fitness estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Grid computing for parallel bioinspired algorithms
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Multi-Objective Optimization using Grid Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Standardization of an API for Distributed Resource Management Systems
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Future Generation Computer Systems
Globus toolkit version 4: software for service-oriented systems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
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A Grid-enabled asynchronous metamodel-assisted evolutionary algorithm is presented and assessed on a number of aerodynamic shape optimization problems. An efficient way of implementing surrogate evaluation models or metamodels (artificial neural networks) in the context of an asynchronous evolutionary algorithm is proposed. The use of metamodels relies on the inexact pre-evaluation technique already successfully applied to synchronous (i.e. generation-based) evolutionary algorithms, which needs to be revisited so as to efficiently cooperate with the asynchronous search method. The so-created asynchronous metamodel-assisted evolutionary algorithm is further enabled for Grid Computing. The Grid deployment of the algorithm relies on three middleware layers: GridWay, Globus Toolkit and Condor. Single- and multi-objective CFD-based designs of isolated airfoils and compressor cascades are handled using the proposed algorithm and the gain in CPU cost is demonstrated.