On genetic algorithms

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On genetic algorithms

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Annual Workshop on Computational Learning Theory archive
Proceedings of the eighth annual conference on Computational learning theory table of contents

Santa Cruz, California, United States

Pages: 230 - 239

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Eric B. Baum	NEC Research Institute, 4 Independence Way, Princeton, NJ
Dan Boneh	NEC Research Institute, 4 Independence Way, Princeton, NJ
Charles Garrett	NEC Research Institute, 4 Independence Way, Princeton, NJ

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 2, Downloads (12 Months): 18, Citation Count: 4

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references cited by index terms collaborative colleagues

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	N. Alon, J. H. Spencer: The probabilistic Method. John Wiley and Sons, New York, (1992).
	2	Thomas Bäck , Hans-Paul Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation, v.1 n.1, p.1-23, Spring 1993
	3	E. B. Baum. "On genetic algorithms", NEC Internal Report June 1994.
	4	Eric B. Baum , Yuh-Dauh Lyuu, The transition to perfect generalization in perceptrons, Neural Computation, v.3 n.3, p.386-401, Fall 1991
	5	V. Chvatal, "MasterMind", Combinatorica 3 (1983), pp. 325-329.
	6	Stephanie Forrest , Melanie Mitchell, What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation, Machine Learning, v.13 n.2-3, p.285-319, Nov./Dec. 1993 [doi>10.1007/BF00993046]
	7	David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
	8	David Haussler , H. Sebastian Seung , Michael Kearns , Naftali Tishby, Rigorous learning curve bounds from statistical mechanics, Proceedings of the seventh annual conference on Computational learning theory, p.76-87, July 12-15, 1994, New Brunswick, New Jersey, United States [doi>10.1145/180139.181018]
	9	W. Daniel Hillis, Co-evolving parasites improve simulated evolution as an optimization procedure, Physica D, v.42 n.1-3, p.228-234, June 1990
	10	W. Hoeffding: Probability inequalities for sums of bounded variables. J. American Statistical Association, 58:13-30, 1963.
	11	John H. Holland, Properties of the Bucket Brigade, Proceedings of the 1st International Conference on Genetic Algorithms, p.1-7, July 01, 1985
	12	John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
	13	S-K Ma: Modern Theory of Critical Phenomena,W. A Benjamin, Reading MA 1976.
	14	M. Mitchell, J. H. Holland, S. Forrest: When will a genetic algorithm outperform hillclimbing, in Advances in Neural Information Processing Systems 6, eds. J. D. Cowan, G. Tesauro, and J. Alspector, Morgan Kaufmann, San Mateo CA, (1994) pp 51-58.
	15	Y. Rabinovich: Quadratic dynamical systems and theoretical aspects of genetic algorithm, Ph.D. Thesis, Hebrew university of Jerusalem, 1993.
	16	I. Rechenberg: "Cybernetic solution path of an experimental problem", ROy. Aircr. Establ., libr. transl. 1122. Hants, UK.: Farnborough (1965)
	17	I. Rechenberg: "Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution", Stuttgart: Fromlnann-Holzboog (1973).
	18	A. Renyi: Collected works, pp. 633-638.
	19	Y. Rabinovich, A. Sinclair, A. Wigderson: Quadratic dynamical systems, Proceedings of the 33rd IEEE FOCS (1992), pp. 304-313.
	20	C. E. Shannon (1951) Prediction and entropy of printed English, Bell System Technical J. pp50-64, January.
	21	Hans-Paul Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons, Inc., New York, NY, 1981
	22	Santosh S. Venkatesh, On learning binary weights for majority functions, Proceedings of the fourth annual workshop on Computational learning theory, p.257-266, August 05-07, 1991, Santa Cruz, California, United States
	23	Shao C. Fang , Santosh S. Venkatesh, On the average tractability of binary integer programming and the curious transition to perfect generalization in learning majority functions, Proceedings of the sixth annual conference on Computational learning theory, p.310-316, July 26-28, 1993, Santa Cruz, California, United States [doi>10.1145/168304.168360]

CITED BY 4

	David Pointcheval , Guillaume Poupard, A New $\mathcal{NP}$ -Complete Problem and Public-Key Identification, Designs, Codes and Cryptography, v.28 n.1, p.5-31, January 2003
	Adam Prügel-Bennett, When a genetic algorithm outperforms hill-climbing, Theoretical Computer Science, v.320 n.1, p.135-153, 12 June 2004
	Eric B. Baum, Toward a Model of Intelligence as an Economy of Agents, Machine Learning, v.35 n.2, p.155-185, May 1999
	Eric B. Baum , Dan B. Boneh , Charles B. Garrett, Where Genetic Algorithms Excel, Evolutionary Computation, v.9 n.1, p.93-124, January 2001