Instance-Based Learning Algorithms
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
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Learning with Genetic Algorithms: An Overview
Machine Learning
Genetic Programming of Minimal Neural Nets Using Occam's Razor
Proceedings of the 5th International Conference on Genetic Algorithms
Combining Genetic Algorithms with Memory Based Reasoning
Proceedings of the 6th International Conference on Genetic Algorithms
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We propose a method for evolving strategies based on the nearest-neighbor rule. A strategy corresponds to an action selection function which chooses an action depending on the current state of a (reactive) control problem to be solved. Given a set I of state/action pairs, our approach determines the action to be taken in state S' as the action A from (S, A) ∈ I for which S' and S are "nearest" among all states from members of I. The set I is evolved using a genetic algorithm. Our approach exceeds "standard" condition/action rule based approaches regarding the ways the state space can be subdivided. This is achieved without confronting the genetic algorithm---unlike neural networks---with too hard search problems as preliminary experiments with variations of a pursuit (predator--prey) game demonstrate.