Calibration Agent for Ecological Simulations: A Metaheuristic Approach

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Publication Details

Author list: Valente, P., Pereira, A., & Reis, L. P.
Publisher: Universidade do Porto - Faculdade de Engenharia
Place: Porto
Publication year: 2008
Languages: English-Great Britain (EN-GB)


Abstract

This paper presents an approach to the calibration of ecological models,
using intelligent agents with learning skills and optimization
techniques. Model calibration, in complex ecological simulations is
tipically performed by comparing observed with predicted data and it
reveals as a key phase in the modeling process. It is an interactive
process, because after each simulation, the agent acquires more
information about variables inter-relations and can predict the
importance of parameters into variables results. Agents may be seen, in
this context, as self-learning tools that simulate the learning process
of the modeler about the simulated system. As in common Metaheuristics,
this self-learning process, initially involves analyzing the problem and
verifying its inter-relationships. The next stage is the learning
process to improve this knowledge using optimization algorithms like
Hill-Climbing, Simulated Annealing and Genetic Algorithms. The process
ends, when convergence criteria are obtained and thus, a suitable
calibration is achieved. Simple experiments have been performed to
validate the approach


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Last updated on 2019-13-08 at 00:46