Abstract
This paper summarizes recent research on competition-based learning procedures performed by the Navy Center for Applied Research in Artificial Intelligence at the Naval Research Laboratory. We have focused on a particularly interesting class of competition-based techniques called genetic algorithms. Genetic algorithms are adaptive search algorithms based on principles derived from the mechanisms of biological evolution. Recent results on the analysis of the implicit parallelism of alternative selection algorithms are summarized, along with an analysis of alternative crossover operators. Applications of these results in practical learning systems for sequential decision problems and for concept classification are also presented.
Sponsored in part by the Office of Naval Research under Work Request N00014-91-WX24011.
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Grefenstette, J.J., De Jong, K.A., Spears, W.M. (1993). Competition-Based Learning. In: Meyrowitz, A.L., Chipman, S. (eds) Foundations of Knowledge Acquisition. The Springer International Series in Engineering and Computer Science, vol 195. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-27366-2_6
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DOI: https://doi.org/10.1007/978-0-585-27366-2_6
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