Implementing behaviour in individual-based models using neural networks and genetic algorithms

作     者：Huse, G Strand, E Giske, J 

作者机构：Univ Bergen Dept Fisheries & Marine Biol N-5020 Bergen Norway 

出 版 物：《EVOLUTIONARY ECOLOGY》 (进化生态学)

年 卷 期：1999年第13卷第5期

页      面：469-483页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0713[理学-生态学] 

基　　金：Norges Forskningsråd 

主　　题：adaptation artificial neural networks behaviour genetic algorithms habitat choice individual-based model state dependence stochastic dynamic programming 

摘      要：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.