A multiclass nonparametric partitioning algorithm
Pattern Recognition Letters
Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
The evolution of behavior: some experiments
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Machine learning: an artificial intelligence approach volume III
Machine learning: an artificial intelligence approach volume III
Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Machine Learning
Optimizing Neural Networks Using FasterMore Accurate Genetic Search
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
On the State of Evolutionary Computation
Proceedings of the 5th International Conference on Genetic Algorithms
Exploring Adaptive Agency III: Simulating the Evolution of Habituation and Sensitization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Co-evolutionary Constraint Satisfaction
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Genetic state-space search for constrained optimization problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
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Discusses various avenues for exploiting biological learning mechanisms within machine learning. Special attention is given to the following issues: (a) the reasons for the wide variety of biological learning mechanisms; (b) the relation between lifetime and genetic learning; (c) a description of the driving forces of genetic learning and their use in evolutionary computation. Various symbolic machine learning and reasoning techniques can be used to complement (genetic and/or neural) sub-symbolic learning. A first approach uses symbolic induction for explaining the behavior of (genetically evolved) neural nets. Next, a general framework for the use of (symbolic) domain knowledge during genetic learning is introduced.