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Self-adaptive constructivism in Neural XCS and XCSF
2008
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08
For artificial entities to achieve high degrees of autonomy they will need to display appropriate adaptability. In this sense adaptability includes representational flexibility guided by the environment at any given time. This paper presents the use of constructivism-inspired mechanisms within a neural learning classifier system which exploits parameter self-adaptation as an approach to realize such behaviour. The system uses a rule structure in which each is represented by an artificial neural
doi:10.1145/1389095.1389364
dblp:conf/gecco/HowardBL08
fatcat:mlc63vrhfnc5jbzbsrjjnj6m2i