Learning with genetic algorithms: An overview

摘要

Genetic algorithms represent a class of adaptive search techniques that have been intensively studied in recent years. Much of the interest in genetic algorithms is due to the fact that they provide a set of efficient domain-independent search heuristics which are a significant improvement over traditional “weak methods” without the need for incorporating highly domain-specific knowledge. There is now considerable evidence that genetic algorithms are useful for global function optimization and NP-hard problems. Recently, there has been a good deal of interest in using genetic algorithms for machine learning problems. This paper provides a brief overview of how one might use genetic algorithms as a key element in learning systems.