Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Optimization Techniques on Computational Grids
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Grids and grid technologies for wide-area distributed computing
Software—Practice & Experience
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
On the scalability of parallel genetic algorithms
Evolutionary Computation
A genetic-fuzzy approach for mobile robot navigation among moving obstacles
International Journal of Approximate Reasoning
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design.