Implementing behaviour in individual-based models using neural networks and genetic algorithms |
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Authors: | Geir Huse Espen Strand Jarl Giske |
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Institution: | (1) Department of Fisheries and Marine Biology, University of Bergen, Norway |
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Abstract: | Even though individual-based models (IBMs) have become very popular in ecology during the last decade, there have been few
attempts to implement behavioural aspects in IBMs. This is partly due to lack of appropriate techniques. Behavioural and life
history aspects can be implemented in IBMs through adaptive models based on genetic algorithms and neural networks (individual-based-neural
network-genetic algorithm, ING). To investigate the precision of the adaptation process, we present three cases where solutions
can be found by optimisation. These cases include a state-dependent patch selection problem, a simple game between predators
and prey, and a more complex vertical migration scenario for a planktivorous fish. In all cases, the optimal solution is calculated
and compared with the solution achieved using ING. The results show that the ING method finds optimal or close to optimal
solutions for the problems presented. In addition it has a wider range of potential application areas than conventional techniques
in behavioural modelling. Especially the method is well suited for complex problems where other methods fail to provide answers.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | adaptation artificial neural networks behaviour genetic algorithms habitat choice individual-based model state dependence stochastic dynamic programming |
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