Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/15882
Author(s): Pedro Valente
António Pereira
Luis Paulo Reis
Title: Calibration Agent for Ecological Simulations: A Metaheuristic Approach
Issue Date: 2008
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
Description: 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
Subject: Ciências tecnológicas
Tecnologia de agentes
Tecnologia do conhecimento
Tecnologia
URI: http://hdl.handle.net/10216/15882
Document Type: Artigo em Livro de Atas de Conferência Nacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Nacional

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