Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
Exploiting Context When Learning to Classify
ECML '93 Proceedings of the European Conference on Machine Learning
The Pragmatic Roots of Context
CONTEXT '99 Proceedings of the Second International and Interdisciplinary Conference on Modeling and Using Context
Evolution of Cooperative Problem Solving in an Artificial Economy
Neural Computation
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Genetic Programming is extended so that the solutions being evolved do so in the context of local domains within the total problem domain. This produces a situation where different "species" of solution develop to exploit different "niches" of the problem - indicating exploitable solutions. It is argued that for context to be fully learnable a further step of abstraction is necessary. Such contexts abstracted from clusters of solution/model domains make sense of the problem of how to identify when it is the content of a model is wrong and when it is the context. Some principles of learning to identify useful contexts are proposed.