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On genetic algorithms

On genetic algorithms On Genetic (Extended Algorithms* Abstract) Eric ericdresearch. B. Baum nj .nec. com Dan Boneh .edu Institute Way 085.40 NJ Charles garrettQresearch.nj Garrett .nec.com dabo~cs.princeton NEC Research 4 Independence Princeton, Abstract We analyze Algorithm algorithms the performance we call Culling on a problem of a Genetic and a variety we refer to Type of other as ASP. Consider ˜Additive have The For Our strings to we new to ter  ately it about tion and This of on shown hill suited in time common the following Search which problem, Let returns which X ~ we call {1,2, ASPl for We Problem ™. .,., L}N. vector an oracle objective simplicity, genetic drawn strings. œbreed  the number a target N > L. of components t c X. as possible, to a query z E .%- and is to find we always approach randomly random The last top t with is to from as few queries Culling is near noise tolerant, in some of learning noisy into ASP. when of a rigorous methods. To analyze new the the optimal for this problem, highly and the best known a~~roach .. We Ising show that the problem perception results C,A ™s provide and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

On genetic algorithms

Association for Computing Machinery — Jul 5, 1995

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References (21)

Datasource
Association for Computing Machinery
Copyright
Copyright © 1995 by ACM Inc.
ISBN
0-89791-723-5
doi
10.1145/225298.225326
Publisher site
See Article on Publisher Site

Abstract

On Genetic (Extended Algorithms* Abstract) Eric ericdresearch. B. Baum nj .nec. com Dan Boneh .edu Institute Way 085.40 NJ Charles garrettQresearch.nj Garrett .nec.com dabo~cs.princeton NEC Research 4 Independence Princeton, Abstract We analyze Algorithm algorithms the performance we call Culling on a problem of a Genetic and a variety we refer to Type of other as ASP. Consider ˜Additive have The For Our strings to we new to ter  ately it about tion and This of on shown hill suited in time common the following Search which problem, Let returns which X ~ we call {1,2, ASPl for We Problem ™. .,., L}N. vector an oracle objective simplicity, genetic drawn strings. œbreed  the number a target N > L. of components t c X. as possible, to a query z E .%- and is to find we always approach randomly random The last top t with is to from as few queries Culling is near noise tolerant, in some of learning noisy into ASP. when of a rigorous methods. To analyze new the the optimal for this problem, highly and the best known a~~roach .. We Ising show that the problem perception results C,A ™s provide and

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