Eric B. Baum, Dan Boneh, Charles Garrett  Home/Search
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Abstract: We analyze the performance of a Genetic Algorithm (GA) we call Culling and a variety of other algorithms on a problem we refer to as Additive Search Problem (ASP). ASP is closely related to several previously well studied problems, such as the game of Mastermind and additive fitness functions. We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Culling is efficient on ASP, highly noise tolerant, and the best known approach in some regimes.... (Update)
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BibTeX entry: (Update)
Baum, E. B., Boneh, D., and Garrett, C. (1995). Where genetic algorithms excel. In Proceedings of the Conference on Computational Learning Theory, New York. Association for Computing Machinery. http://citeseer.ist.psu.edu/baum95where.html More
@article{ baum01where,
author = "Baum and Boneh and Garrett",
title = "Where Genetic Algorithms Excel",
journal = "EVCOMP: Evolutionary Computation",
volume = "9",
year = "2001",
url = "citeseer.ist.psu.edu/baum95where.html" }
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[Article contains additional citations not shown here]
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