A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet
Future Generation Computer Systems - Special issue: Bio-inspired solutions to parallel processing problems
Neural Networks: A Comprehensive Foundation
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Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
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Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A framework for adaptive execution in grids
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Enhancing wildland fire prediction on cluster systems applying evolutionary optimization techniques
Future Generation Computer Systems
Implementation and utilisation of a Grid-enabled problem solving environment in Matlab
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
Design and Analysis of Experiments
Design and Analysis of Experiments
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
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
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
International Journal of High Performance Computing Applications
A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization
Genetic Programming and Evolvable Machines
Modelling and Simulation in Engineering
A hierarchical distributed evolutionary algorithm to TSP
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Expert Systems with Applications: An International Journal
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A Grid-enabled optimization environment is presented. It is based on Metamodel-Assisted Evolutionary Algorithms (MAEAs), where radial basis function networks, trained on the fly on selected subsets of the previously evaluated individuals, are used to pre-evaluate the population members. The search follows a Hierarchical and Distributed scheme (HDMAEA), with more than one search level, each of which is associated with a different problem-specific evaluation tool and a different number of semi-isolated demes. Irrespective of the use of cluster or Grid computing, the HDMAEA drastically reduces the number of evaluations required to reach the optimal solution(s). The Grid-enabled HDMAEA, based on the master-slave model with simultaneously evaluated population members, aims at solving large scale optimization problems in affordable wall clock time. In the proposed Grid-computing setup, Condor is used as the local resource manager on each contributing cluster, authentication and interfacing is carried out via the Globus Toolkit and the unification of Grid resources under a common queue is undertaken by the Gridway metascheduler. If more than one search level are used (hierarchical search), the optimization of Grid resources' allocation relies on the distinction between computationally demanding, high-accuracy and less demanding, low-accuracy evaluation tools. The proposed Grid-enabled problem solving environment is demonstrated on three aerodynamic shape optimization problems, namely the design of a compressor cascade and two 3D elbow ducts, on three remote clusters.