Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Grid-Enabled Problem Solving Environment (PSE) for Design Optimisation within Matlab
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
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Complex environmental modeling often involves a large number of unknown physical and ecological parameters. Parameter estimation is one of the most difficult steps in many modeling activities. In this paper we present a service-oriented framework, named GGPE-G (Grid-enabled Global optimization for General Parameter Estimation), for efficient parameter estimation in heterogeneous, distributed systems. Being presented as services, the optimization algorithms, the physical and ecological process models and clients can interact with each other by XML message interactions. The proposed approach supports a generic parameter estimation procedure and can be easily applied to different modeling environment. In this paper, we explain the design, architecture, and implementation of GGPE-G in details. We also apply GGPE-G to a complex soil-water-atmosphere-plant modeling system to demonstrate its utility and efficiency.