Artificial Intelligence
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning qualitative models of dynamic systems
Artificial intelligence in mathematics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
The art of artificial intelligence: I. Themes and case studies of knowledge engineering
The art of artificial intelligence: I. Themes and case studies of knowledge engineering
Nonlinearities in genetic adaptive search.
Nonlinearities in genetic adaptive search.
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
The Knowledge Engineering Review
An immune-inspired approach to qualitative system identification of biological pathways
Natural Computing: an international journal
Learning qualitative models from numerical data
Artificial Intelligence
Intermediate depth representations
Artificial Intelligence in Medicine
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A genetic algorithm is used for learning qualitative model* based on the QSIM formalism. Hierarchical representation enables formation of "submodels" relevant for induction of domain explanation. Daring the search for better coding of the candidates, in parallel with the search for better solutions, the sise and shape of candidate solutions are dynamically created. Optimisation is based on the maximisation of the number of examples covered by a candidate solution combined with the minimisation of the number of constraints used in the solution. The result of learning is a set of models of different specificity that explain all given examples. An experiment in learning a qualitative model of the connected container system (U-TUBE) is described in detail. Several solutions, equivalent to the original model, were discovered.